{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze Big Financial Data**\n",
    "\n",
    "O'Reilly (2014)\n",
    "\n",
    "Yves Hilpisch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"border:0px solid grey;\" src=\"http://hilpisch.com/python_for_finance.png\" alt=\"Python for Finance\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Buy the book ** |\n",
    "<a href='http://shop.oreilly.com/product/0636920032441.do' target='_blank'>O'Reilly</a> |\n",
    "<a href='http://www.amazon.com/Yves-Hilpisch/e/B00JCYHHJM' target='_blank'>Amazon</a>\n",
    "\n",
    "**All book codes & IPYNBs** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**The Python Quants GmbH** | <a href='http://tpq.io' target='_blank'>http://tpq.io</a>\n",
    "\n",
    "**Contact us** | <a href='mailto:pff@tpq.io'>pff@tpq.io</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introductory Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import plt\n",
    "plt.style.use('seaborn')\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implied Volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "uuid": "8e3ac03d-e5c1-4184-8494-5c02f5c1a897"
   },
   "outputs": [],
   "source": [
    "# 参考日期指数的收盘价（将S改为V，表示现在处理的是波动率指数）  \n",
    "V0 = 17.6639"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "uuid": "3d727b16-4ff1-49fb-a54a-6e96568f54b0"
   },
   "outputs": [],
   "source": [
    "# 无风险利率，假定年率r=0.01\n",
    "r = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "uuid": "009042d8-f384-482c-8b4b-eeac6312315e"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "h5 = pd.HDFStore('./source/vstoxx_data_31032014.h5', 'r')\n",
    "futures_data = h5['futures_data']  # VSTOXX futures data\n",
    "options_data = h5['options_data']  # VSTOXX call option data\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "496   2014-03-31\n",
       "497   2014-03-31\n",
       "498   2014-03-31\n",
       "499   2014-03-31\n",
       "500   2014-03-31\n",
       "501   2014-03-31\n",
       "502   2014-03-31\n",
       "503   2014-03-31\n",
       "Name: DATE, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futures_data['DATE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "496   2014-04-18\n",
       "497   2014-05-16\n",
       "498   2014-06-20\n",
       "499   2014-07-18\n",
       "500   2014-08-15\n",
       "501   2014-09-19\n",
       "502   2014-10-17\n",
       "503   2014-11-21\n",
       "Name: MATURITY, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futures_data['MATURITY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46170   2014-03-31\n",
       "46171   2014-03-31\n",
       "46172   2014-03-31\n",
       "46173   2014-03-31\n",
       "46174   2014-03-31\n",
       "           ...    \n",
       "46560   2014-03-31\n",
       "46561   2014-03-31\n",
       "46562   2014-03-31\n",
       "46563   2014-03-31\n",
       "46564   2014-03-31\n",
       "Name: DATE, Length: 395, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_data['DATE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46170   2014-04-18\n",
       "46171   2014-04-18\n",
       "46172   2014-04-18\n",
       "46173   2014-04-18\n",
       "46174   2014-04-18\n",
       "           ...    \n",
       "46560   2014-11-21\n",
       "46561   2014-11-21\n",
       "46562   2014-11-21\n",
       "46563   2014-11-21\n",
       "46564   2014-11-21\n",
       "Name: MATURITY, Length: 395, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_data['MATURITY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# futures_data['DATE'] = futures_data['DATE'].apply(lambda x: dt.datetime.fromtimestamp(x / 1e9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# futures_data['MATURITY'] = futures_data['MATURITY'].apply(lambda x: dt.datetime.fromtimestamp(x / 1e9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "uuid": "8d5c855d-87d0-4108-93c7-57094610bd68"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>EXP_YEAR</th>\n",
       "      <th>EXP_MONTH</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>MATURITY</th>\n",
       "      <th>TTM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>17.85</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>19.55</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>19.95</td>\n",
       "      <td>2014-06-20</td>\n",
       "      <td>0.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>20.40</td>\n",
       "      <td>2014-07-18</td>\n",
       "      <td>0.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>20.70</td>\n",
       "      <td>2014-08-15</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>20.95</td>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>21.05</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>0.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>21.25</td>\n",
       "      <td>2014-11-21</td>\n",
       "      <td>0.644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  EXP_YEAR  EXP_MONTH  PRICE   MATURITY    TTM\n",
       "496 2014-03-31      2014          4  17.85 2014-04-18  0.049\n",
       "497 2014-03-31      2014          5  19.55 2014-05-16  0.126\n",
       "498 2014-03-31      2014          6  19.95 2014-06-20  0.222\n",
       "499 2014-03-31      2014          7  20.40 2014-07-18  0.299\n",
       "500 2014-03-31      2014          8  20.70 2014-08-15  0.375\n",
       "501 2014-03-31      2014          9  20.95 2014-09-19  0.471\n",
       "502 2014-03-31      2014         10  21.05 2014-10-17  0.548\n",
       "503 2014-03-31      2014         11  21.25 2014-11-21  0.644"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futures_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options_data['DATE'] = options_data['DATE'].apply(lambda x: dt.datetime.fromtimestamp(x / 1e9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options_data['MATURITY'] = options_data['MATURITY'].apply(lambda x: dt.datetime.fromtimestamp(x / 1e9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "uuid": "48b29f70-8142-4960-8d4d-3a241685bc1d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 395 entries, 46170 to 46564\n",
      "Data columns (total 8 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   DATE       395 non-null    datetime64[ns]\n",
      " 1   EXP_YEAR   395 non-null    int64         \n",
      " 2   EXP_MONTH  395 non-null    int64         \n",
      " 3   TYPE       395 non-null    object        \n",
      " 4   STRIKE     395 non-null    float64       \n",
      " 5   PRICE      395 non-null    float64       \n",
      " 6   MATURITY   395 non-null    datetime64[ns]\n",
      " 7   TTM        395 non-null    float64       \n",
      "dtypes: datetime64[ns](2), float64(3), int64(2), object(1)\n",
      "memory usage: 27.8+ KB\n"
     ]
    }
   ],
   "source": [
    "options_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "uuid": "dead6085-55a7-46c1-9396-3ec0e247b8f4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>MATURITY</th>\n",
       "      <th>TTM</th>\n",
       "      <th>STRIKE</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46170</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46171</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46172</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46173</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46174</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>0.049</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE   MATURITY    TTM  STRIKE  PRICE\n",
       "46170 2014-03-31 2014-04-18  0.049     1.0  16.85\n",
       "46171 2014-03-31 2014-04-18  0.049     2.0  15.85\n",
       "46172 2014-03-31 2014-04-18  0.049     3.0  14.85\n",
       "46173 2014-03-31 2014-04-18  0.049     4.0  13.85\n",
       "46174 2014-03-31 2014-04-18  0.049     5.0  12.85"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_data[['DATE', 'MATURITY', 'TTM', 'STRIKE', 'PRICE']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "uuid": "808a0269-42d5-43ed-b753-28abb46820de"
   },
   "outputs": [],
   "source": [
    "options_data['IMP_VOL'] = 0.0\n",
    "  # new column for implied volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "uuid": "233c9f45-d3c4-48f4-8422-7e77c490d232"
   },
   "outputs": [],
   "source": [
    "from bsm_functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "uuid": "8da4e671-cbbc-44fd-8050-c8fe300a4501"
   },
   "outputs": [],
   "source": [
    "tol = 0.5  # tolerance level for moneyness\n",
    "for option in options_data.index:\n",
    "    # iterating over all option quotes\n",
    "    forward = futures_data[futures_data['MATURITY'] == \\\n",
    "                options_data.loc[option]['MATURITY']]['PRICE'].values[0]\n",
    "      # picking the right futures value\n",
    "    if (forward * (1 - tol) < options_data.loc[option]['STRIKE']\n",
    "                             < forward * (1 + tol)):\n",
    "        # only for options with moneyness within tolerance\n",
    "        imp_vol = bsm_call_imp_vol(\n",
    "                V0,  # VSTOXX value \n",
    "                options_data.loc[option]['STRIKE'],\n",
    "                options_data.loc[option]['TTM'],\n",
    "                r,   # short rate\n",
    "                options_data.loc[option]['PRICE'],\n",
    "                sigma_est=2.,  # estimate for implied volatility\n",
    "                it=100)\n",
    "        options_data.loc[option, 'IMP_VOL'] = imp_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "uuid": "94491814-c0c6-4565-b1f7-cedb1af12c48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "496   2014-04-18\n",
       "497   2014-05-16\n",
       "498   2014-06-20\n",
       "499   2014-07-18\n",
       "500   2014-08-15\n",
       "501   2014-09-19\n",
       "502   2014-10-17\n",
       "503   2014-11-21\n",
       "Name: MATURITY, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futures_data['MATURITY']\n",
    "  # select the column with name MATURITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "uuid": "cbb44f1e-2ab5-4d8f-976a-2d36515eef12"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE         2014-03-31 00:00:00\n",
       "EXP_YEAR                    2014\n",
       "EXP_MONTH                      4\n",
       "TYPE                           C\n",
       "STRIKE                       1.0\n",
       "PRICE                      16.85\n",
       "MATURITY     2014-04-18 00:00:00\n",
       "TTM                        0.049\n",
       "IMP_VOL                      0.0\n",
       "Name: 46170, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_data.loc[46170]\n",
    "  # select data row for index 46170"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "uuid": "e267720a-901b-42fd-86eb-f31c29ebc166"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_data.loc[46170]['STRIKE']\n",
    "  # select only the value in column STRIKE\n",
    "  # for index 46170 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "uuid": "03092953-5496-4500-9cf5-e3cbcf46d396"
   },
   "outputs": [],
   "source": [
    "plot_data = options_data[options_data['IMP_VOL'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "uuid": "738b9618-b3a9-434f-a685-441a0c837b76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2014-04-18 00:00:00'),\n",
       " Timestamp('2014-05-16 00:00:00'),\n",
       " Timestamp('2014-06-20 00:00:00'),\n",
       " Timestamp('2014-07-18 00:00:00'),\n",
       " Timestamp('2014-08-15 00:00:00'),\n",
       " Timestamp('2014-09-19 00:00:00'),\n",
       " Timestamp('2014-10-17 00:00:00'),\n",
       " Timestamp('2014-11-21 00:00:00')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maturities = sorted(set(options_data['MATURITY']))\n",
    "maturities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "uuid": "8886807a-ca71-48d0-b5ef-ab7cb9470548"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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69HCteHv16nPVMdvL9ZVXXnHIgZytsPDyK86OwRZvbw8KCy/bta3FzZtNGdsI8goi0ifc4MhuHFdzDYTjyfl3vtZ0DRIS4nn++TmUlZWxcuUyVq1aQXh4e2bOvIslS77l2LFEDh06wOOP/xJPTy+goprcpk0bOH36FKWlpURHx1Qfb9euHaxevZLU1BRcXExER/eo1Z63twVPT0+WL1/Kzp3bGThwMIMHDwWgZ8/efPXVZyQlnWD37h384he/xNfXt8F4y8vLWbZsSXW83bopevfuYzXmxnh7e7xqbZ1h1eSaWkusJmdNeXk5b+z4K24mV14Y+LRMmXMQqaTlXHL+nU+ugfM1ZTU5ecmuGaqYMjectPwMjp9LdnY4QgghWiDp4JupQWE342n2ZJ1UmRNCCHENpINvpjxc3RkePoj9Zw6RW5Tn7HCEEEK0MNLBN2Oj2g+lvLycDSe3OjsUIYQQLYx08M1YoGcAfYJ7sjljO5dLi50djhBCiBZEOvhmLjZiOAXFhezK3OvsUIQQQrQgkuimmevm15lw7zDWpW9maLuBMmVOCCFoXtXkfvazB/DyujJvPTMzk6+/Xlxv/5ycbObOfZNDh44wf/7H1csTEuL59NMFREfHcOTIYWbNmk2vXn2u+xxJB9/MmUwmYiOH83nCNxzLO0E3/y7ODkkIIZyuOVWTu//+nzB27ASgInXugQP7GjzGgQP7GDt2LAcPHq61/P333+X222cwevQY1q9fy/z5/2Lu3H9e66mpJh18CzAw9CYWH1vJuvTN0sELIZoVffA0CQdOOfSY0X3aoXqH2dymuVSTi4yMqu7cARYv/oZf/eqFBo8xZsw4Tpw4Um95QEBA9ReLvLxclIqpt821kGfwLYC7qzvD2w9m/5nD5Fxs+NulEELcqJxZTa6mkyfT8fa24Ofnd1XxP/LIL1i1ajnvvDOXVauWMWbMuKva3xoZwbcQI9sP4YfU9Ww8uZXpXac4OxwhhABA9Q5rdLRtJGdXk6tp4cKvuPPOe6r/bqiaXENefPEZnnnmBXr16sPx48eYM+dxliyJu+53rqSDbyEC2vjTN6hiytyUTuNwd3V3dkhCCOFUzaGaXJWCgnwyM0/TuXPX6mXWXtarKysrk8DAIAACA4MoLnZMQSCpJtdEHFHFqa1HW3Yc30CHpCxCwrqBu3TyV6M1VdJqieT8O19rugbNqZocwKJFC+nffxBRUR2sxrx3727i4lYQHx9PUVERMTE9MJvNtG8fyddff0l6eiqrV6/ggQceomPHTnadB6km1ww4pIJQ/gXKRvUlND2bkm7dyY1bBxaLQ+K7EUglLeeS8+98cg2cT6rJiQaZdQKh6dkVvx9NxKzjnRyREEKI5ko6+BakRMVQ0q0bAFmRIZQ4aCqFEEKI1kdesmtJLBZy49azb8MXfMwRnizNpjNyi14IIUR9hnXwSqkuwBvAHiACyNFav1ZnmxeAMOA00B94WWudULkuGUiu3PSk1vp+o2JtUSwWYib+FPOWPxGXvJZf9H3I2REJIYRohowcwQcAX2qtFwMopY4opZZrrXfX2MYCPKO1LldK3QP8Bbitct0CrfUrBsbXYnm4ujMmYgTLklaTfiGDCJ9wZ4ckhBCimTGsg9da76yzyAUoqLPNS3XW59f4e5RS6nnAB1iptd5iSKAt1OiIYXyfuo7VKWv5WS+5uSGEEKK2JnkGr5SaAcRV3X5vYL078FPgiRqLX9Ra71BKeQF7lFJTtdbHrLXh7++F2ezq0LgdLTjYx4FH82Fit9Es1T/w0zYzCfMJceCxWy/HXgNxteT8O19ruQapqanMnTuXHj16cPr0afz8/HjyySfJy8vjrbfeIjIykuTkZJ555hmCgiqSyBw6dIg333yT3r1788ILtfPFl5WV8fDDD2OxWHj77bcbbHPx4sXEx8fj4uJCVFQU995bkd5248aNLFq0CKUU+/bt49e//jWdOtWfx37mzBl+97s3SUhI4Jtvvqle/o9//IMdO3ZU//3YY48xfLjtBD32MLyDV0qNAcYAc6ysdwfeBX6ntT5etVxrvaPy30Kl1D5gOGC1g8/NLXRk2A5nxPzTIYGDWWFay1f7ljMr+k6HHrs1kjnAziXn3/la0zVITs5gxIgxtarJ9e07iKVLv+PmmwdWV5N77bU3qqvJ7d59gJ49+1JYWFTvPHz66QKCg8PIy8tt8BxlZWXy73/Pr1VNrnv33kRGRvH662/whz+8Qffu0Vy+XMa8ef/ihRd+X+8Ya9duqq4mV7ONgoJL/PWvtavH2XudbH1hM7SDV0rdCowEngbaKaU6ABoo0VqfV0p5Av8E/k9rfVgpdYfW+hul1FjATWu9qvJQXYHjDbVxI/P1aMuQdgPYlrGTKZ3G4+fh6+yQhBA3mPyc/RScbbg86rXyDuiHJbCvzW2aUzU5f/8A8vLyAMjLy0Op6AaPYa2aHMBHH32Am5s7ZWWl3HnnvbRp08bm57eHYfPglVL9ga+AIcBaYDGggBeBxys3+4yKkfk8pdS6ynUAWcAjSqnfKqXeAb7RWm8yKtaWbHzUaMooZ03qBmeHIoQQTuHsanJPP/0cH374Pv/4x984eHA/AwcOuar4x4wZx913z2LWrNl4eXnzt7/Zl8O+MUa+ZLcbbE/S1lrPtLL8IGC99I6oFuQZSP+QvmzK2M7EjrdgcfNufCchhHAQS2DfRkfbRnJ2Nbni4mKef34O7733ISEhoWzatIFXXvkt77//sd3V5Dp37lL9e//+A/nii0+u97QAkuimVZjQYQw7M/eyPm0zt3ae4OxwhBCiSTSHanLFxZc5f/4cfn7+AAQFBXH5ckVBH3uryc2b93eeeOJpANLSUmnfPsLeU2CTVJNrIkZWcfJxt5B24SR7sw4wqv1QzC7yva0hramSVksk59/5WtM1aC7V5Nzc3PH19WPRom9ISUliw4a1/OxnjxEWFlYvZmvV5Pbs2cWmTRvQOp7du3fwxBNz8PX1s+s8SDW5ZsDot1eTzqXwf7vnMaPrrYyLGm1YOy1Za3qDuCWS8+98cg2cT6rJiavWybcD3f268GPqBorLrL9QIoQQ4sYgHXwrMrHjLZy7fIHtp3Y5OxQhhBBOJh18K6L8uxLlE8H3qespLSt1djhCCCGcSDr4VsRkMjGx4y1kX8xhb9YBZ4cjhBDCiaSDbwr5+bB9e8W/BusT1IMwrxDiUtbSWl6gFEIIcfWkgzdafj6+sUNhyBD8J8Ya3sm7mFyY0GEMGQWnOZQTb2hbQgghmi+ZMG0ws47HPTWl4vejiZh1PCX9Bxra5oDQfixLWk1c8lp6BcZU504WQojW4uTJdP7973+iVDRZWVn4+vry0EOPcP78Od599x3Cw9uTnp7Ko48+QUBAIFAxd/6dd/5GdHQPnnyydv2zsrIynn32Kby9va0mqImLW0FiosbV1YXw8Ijq7HQ7d25nzZrVREREkpqawpNP/oq2bdvW2z8nJ5u5c9/k0KEjzJ//cfXyxMQEvv76Szp27ERS0gkefvgXDc6jv1oygjdYiYqhpEtXAIqCgilRMY3scf1cXVwZFzWapPMpHMs7YXh7QgjR1M6fP8e4cROYNesnzJnzHGvWrCYhIZ733pvHgAGDmD37QUaOjGXevLnV+5w4cYx+/W5u8Hiff/6xzQxyWVmZfPHFpzz55Bwef/xpli1bTFpaKqWlpbz66u/4xS+e4oEHHqR7d8V//vPvBo9x4MA+xo4dW+/x6Z/+9Bp33XUv99//U2JjxzJ3bjPPRS8qWSzkfr+Bko/nk7xjFxE52bSx2EzR7xBD2w1kZdIPxKWspZt/l8Z3EEKIa7An+zy7s8879Jj9g9pyc1D9EXBNzaWa3LhxE7l06XJ15rnw8AgWL/62wWNYqyaXnp5KaGhY5f7t2b17p83Pbi8ZwTcFi4Xg554BX1+yv/umSZp0d3XjlsiRxJ9NJPV8epO0KYQQzuDManL+/v4EBwdz4kRFRfP4+MMUFBRcVfx9+vTj8OFD1fsXFRXZjNleMoJvImZvbwIm30r2wv9SmKjx6q4Mb3NkxBDiUtayOmUtD/eebXh7Qogbz812jLaN5OxqcgB/+cvfWbp0EYGBQXh5eVePxu2tJvfSS6/x9ddfkp6ehpeXF0FBwTZjtlejR1BKva61fum6WxL43TKO3B9Wk/3tQiJf+K3hL795mj0ZHTGM1SlrOV2QRZh3iKHtCSFEU2oO1eSgorN/7LEnAVi8+FsmTpwC2F9NLjs7m5///HEAduzYVr3/9bLnK8JUpVQnYA3whda6yCEt34Bc3N0JvG0aWZ98RMHB/Vj69DO8zTGRI/gxbQPfp65jdszdhrcnhBBNISEhnj/84TcoFcNTTz1KUVERM2fexaOPPsG77/6DtLQUMjJO8sQTV96WX7lyGfv376W4uJjFi79l2rSZ1et27drB5s0bSE1NYdGihUyffmet9kJCQrnvvgd4++2/4uLiwtSp04iMjAIqOnVXV1dCQ9tRWlrCrFk/aTDmvXt3s27danJyslmwYD733fcAHh5t2Lx5A19//QUdO3amoCCfhx562CHnqNFqckqp3lrrg0qpccB9wBngfa31cYdE4CAtpZpceUkJyS/9Fpc2HkS99ComF+Nfg/hv4mI2ntzKq0NfIKCNv+HtNVdSScu55Pw7n1wD52tu1eSqIjkB5AGPAO8opT5SSvV3QHw3FJPZTOD0GVxKS+PCrh1N0ua4qFEArEnd0CTtCSGEcD57Ovj/KKWWAxuBQqCf1noyFR39C0YG51T5+Zh37zQk85zPwMG4t48gZ9F3lDvgTcnGBLTxZ1DozWzO2MGFy8anyxVCCOF89nTw7YFPgI5a65e01mk1lkcbFpkz5efjM240/pPHGpJe1uTiQtCMOyjOyuTclk0OPbY14zvEUlJWwrq0pmlPCCGEc9nzkt1DWustVX8opUYC57XW+4E+hkXmRGYdT5sTRyt+Nyi9rHfffrTp0pWzSxfTdsgwXNzdHXr8usK8Q+gb3Iv1J7cwrkMsnuY2hrYnhBDCuewZwded/Z8OvGxALM1GiYohp11HAC536WZIelmTyUTQjDsoyc0lb+0ahx+/IRM7jOFiSREbT25tkvaEEEI4j9URvFKq6j3/6Bq/A7gCgYZG5WwWC6eW/sCf3vqWXreOYKpBqWW9omPw6tmLsyuX4zsqFldPT0PaqRLVNoKYgO78mLqR2IgRuLu6GdqeEEII57F1i35M5b8RNX4HKAL+17CImonQqBC8Rg5jdXwu48eU4uHmakg7QTPuJPWNV8hdvYqgaTMMaaOmiR3GMHfve2w7tZNREcMMb08IIYzgjGpy6elpzJs3F1dX11rbnDqVwYIF84mIiOTUqQyefPJXeHl51dvfWjW5X//6aS5evFj99/Hjx1i0aCUeHh7XdY6sdvBa64cAlFKTtdYra65TSl1fqy3EpMFRvPnZHjYfPMUtN1uvMnQ92nTsiKX/AHJXx+F3y1jMPsamfOzq15lObTvwfep6hocPxtXFmC8uQghhpKpqciNHxgLwwAN3MXToCJYu/Y4BAwYxdux4Nm3awLx5c3nppdeBK9Xkiorq52urqiaXl5drtc0jRw4xZMhwdu7cVmv5X/7yvzz88KP06NGLhQu/5LPPPuKRR35Rb/+qanIHDx6utXzSpFsZO3YCUPHF5bPPPrruzh3se8nusFIqqs6yV4GHrrv1Zq5bhC+dw9uyekcasf3a4+JiTGrZwGkzyd+zm9wVywm+5z5D2qhiMpmY2HEM/zqwgF2Z+xjcTlIZCCGu3eaDp9h04JRDjzmiTzuG925nc5umriYHMGHC5Mp0tleUlJSwd+8uYmLeBqB37378+c+vN9jBW6smV9W5Ayxc+BV33nmP1RiuhtWX7JRSCUqpDsBBYB2wvsaP8feSmwGTycSkQVFk5V1kT+IZw9rxCA+n7bAR5K1dQ/HZHMPaqdIrMIZw7zBWp6ylrLzM8PaEEMJITVFNzpq8vDw8PDyqa4t4e3uTm2v9LoAtBQX5ZGaepnPnrte0f122RvDDtdY5Sqk/aa3/XHOFUqr1Jrip4+buwYT4ebJqRyr9VbBhBWICb5/Ghe1byVm6mLCf/syQNlqBlZMAACAASURBVKqYTCYmdhjDh0e+4GD2EfoG9zK0PSFE6zW8d+OjbSM1VTW5UaNiG9zfz8+PS5cuVRehKSgowN+/IiW4vdXkqixbtphbb739Ks+AdbaewedU/vvnBlbfMPOsXFxMTBgUyaerEzmafo7ukX6GtOMWGITv6DHkrV1DwMQpuIeFGdJOlZtC+rD0RBxr41dxc8pFSqNjwKDZAkIIYYSmrCZnjdls5qabBhAff5gePXpx8OA+hg4dAdhfTQ4qHjFs376Nu++eZfc+jcZmbYVSytZc9ynAEIdF0cwN792ORRuTWLU91bAOHiBgylTObdpAzuJvaffo44a1A+Dq4sqk4MHcfNdPCch4jZJu3cmNWyedvBCiRWjqanIAGzeuq97ms88+4v77fwrAr3/9Gz788H127NhGZmYmTz31qwZjtlZNDmDTpg0MHz7CoXeJrVaTU0rtBJZZ2W+K1nqwrQMrpboAbwB7qJhql6O1fq3ONm2A/wNOAt2AN7XWiZXrHgBuAkqB41rr+m9G1GB0NblFG0+wZHMyf3xkMO0Cva96f3srCGV/9w1nly8l6uVXaRPV4VpCtd+ubQRPufJyR+7KNQ7P2NecSCUt55Lz73xyDZyvKavJ2XoG/6LWusEUa5Wdf2MCgC+11osr9zmilFqutd5dY5s5QKrW+v8ppXoDHwAjlVIRwHPATVrrcqXUTqXUj1rro3a0a4hb+kewcnsqcTtSeXCy4zPbVfGfOIm8tT+S8903tH/6GcPaASC6F+c7RdI2KY2Czh0NydgnhBDCOWw9g18D0MAUOYC7gBW2Dqy1rvslwAUoqLPsVuC3ldsfVEr1VUq1BSYCu7XWVaPyrcBkwGkdfFsvd4b3bsemAxnMGNkZX4sxqQBcvbwJmDyF7G++5uLRRDy7dTekHQAsFgp/2My73/yec52jmOPtjTGvEAohhGhqtp7BJ1DR0R4AcqDW//v9uYp58EqpGUCc1jqhzqoQrtSbBzhfuczacqv8/b0wm41N2nLfxGjW7zvJ1oQzzL6GUXxwsI9d2wXcM4NzP/7AuaXfEfmn1w17c78iKB8G3/kY/9zxMcmXTzAoop9xbTUD9l4DYQw5/84n18D5muoaGD5NTik1hopUt3MaWJ0F1PykbSuXZQFd6yw/Zqud3NxCe0O6Zm7ATd2CWb7pBLF9wmjjbk+eoApX+9zFf8ptZH32MSlrt+Dd29iifdFeMYR4BfH5vsV0cO+Ei8meGkQtjzx/dC45/84n18D5DHgGb3Wd1f+TV02TA36ouVwpdTuw156GlVK3UnEX4GkgTCk1VCkVUHkbHmA5MLRy297Afq31eSAO6K+Uqhq6DgVW0gxMGhxFQVGJwzM31eU7chRuQcFkf7uQ8jJjk9G4urhya6cJZBScZk/WAUPbEkII0TTsGar9tM7fG4BGM7EopfoDX1ExnW4tsBhQwItA1RywvwMdlFK/B54F/gdAa51Oxdv1f1NKvQXMd+YLdjV1be9L1whfVu9Mo9TAjtdkNhM4bQaX0lLJ373LsHaq3BzSh3DvMJYnraa0rNTw9oQQQhjL1jS5qnnwU6j9Qp0rMFZrPcLg2K6K0dPkatqTeIZ3vj3IY9N6Migm1K59ruW2THlZGSmvvkx5SQkdX/sjJldj3zHYf+YQ/z74MQ/E3M3QdgMMbcsZ5Pakc8n5d77WdA2aUzW5y5cvs3Dhl8yf/x7Lln3fYCW5qnhvvrkvSUlp1fFWtb1kyXfMn/8v3n773atKVWtrmpytEbzJyk8h8KTdrbdC/boFERrgxcrtqVj7guQIJhcXgqbPpDjzNOc3bzKsnSp9gnoS5dOelUnfU1JmPXezEEI4W1U1uVmzfsKcOc+xZs1qEhLiee+9eQwYMIjZsx9k5MhY5s2bW71PVTW5hlRVk7OlqppcXYcPH2T06Fu4fPlSo/E+/PDDteIFOHYskZ49e9GmTRt7PrrdbE2TexVAKbVSa73Doa22cC4mExMHRfLxKo1OzSO6g79hbXn3u4k2nTuTs3QxPkOH4uLmblhbJpOJqZ0n8c/9H7D11E5Gth9qWFtCiNZh+6ndbD1lT2oU+w1tN7DRSpfNpZocwE03NV6V01q8AN27Rze6/7Vo9Bm81nqHUspFKdVeKRVV+fOhIdG0IMN7hdHWy41VO1INbcdkMhE0405Kcs9ybu1aQ9sC6BHQnc6+HViZtIbLpcWGtyeEENfLmdXkrkXNeI3U6DwvpdR04G0qpqrlAoGA9fsQNwg3syu39I9g0cYkTp7Jp32wcTncvWJ64BXTk7MrluE7ahQubTwNa8tkMnFb54n8fe+/2ZSxjVsiRxrWlhCi5Rvcrn+jo20jObuanC0NVZPbtm1brXiNZM9E7ulAZ+AvWutfKaW8gNca2eeGcMvNEazYlkLcjjR+dquxaV6DZt5B6h9fI/f71QTeNs3Qtrr7d6W7f1dWJ69lePhgPFyNeywghBDXqjlUk7Ol7st6W7Zs4ujRw7Xi7dXLuDwn9kyTy9Bal1CR5wWtdSFw9dVWWiGLpxsje4ez9fBpci8Ye1OjTafOWG7qT27cSkovGP8W7G2dJ3ChOJ/16ZsNb0sIIa5WVTW5w4cP8tRTj/Lii8+SmprCo48+wa5d21mwYD4bNqxtsJrc4cMHWbz421rHq6oml5KSzKJFCxtss6qaXEpKMp999lH18lOnMliwYD5Q8bJeSkqy1Xj3799fK16A8+fPs2DBfPLz81my5DsOHTp4vacHsDFNropSajHwEvATKjr5HGCq1nqQQyJwkKacJldTVt5FfvPeViYNjuKuWOtTGxwxPeVSxknSfvciYTExWB7/peGlXf+5/z8knUvhtWEv4mk27rFAU2lNU4RaIjn/zifXwPmaspqcPSP43wBFwOuAB9Ab+LljQmv5Qvw86a9CWLc3g4uXjJ1a5tHWl5t2bKXT/72J37iRkJ9vaHtTO02gsOQiP6YZP0VPCCGEY9nzFv0RrXWi1vqc1voxrfVdVLxwJypNHhzFxUslbNyfYWg7Zh1PmzNZALidOI5ZxxvaXlTbCPoF9+LH1I3kF9ctBCiEEKI5s1VN7mVr66jIbjfE8eG0TJ3atUVF+rF6Vxq39I/A7GpMsZYSFUNJt+6YjyZSaPHhgosrRt84v7XTBPafOcya1A1M6zLZ4NaEEEI4iq2e6DasZ7OTsuF1TBwcxdnzl9iZkGVcIxYLuXHryFmyisPT7iBryXeGF6IJt4TRP7Qv69I2cf6yPLsTQoiWwtY0uRe11msaWqGUsp7q5wbVp0sg7QK9iNueypAeocbVcLdYKBsyjECzmVP/+ifnNqzDL/YWY9qqNKXTePZkHWB1ylru7Ha7oW0JIYRwDFvlYqs7d6XUJKXU20qpvyulJmqtf2ya8FoOF5OJSYOiSM3K50hKruHtWfoPxLO7Ivu7byg1+GW7UK9gBof1Z+PJbeQW5RnalhBCCMdo9GGxUupZKqbJXQKKgT8opZ4xOrCWaEjPMHy93Vm13dj0tVCRcS7kvgcoKywkZ8l3hrc3ueNYysvLWZUi3+2EEKIlsCeTndJa10oFpJT6j0HxtGhuZhfGDYjgm/UnSMvKJzLE2HnqHpGR+MaOIW/dWnxHxeIREWlYW4GeAQwLH8TmjO2Mj4olyDPAsLaEEEJcP3te927ozSp528qK2Jva4+Hm2iSjeICgaTNx8fQk68vPDS1dCzCp4y24mFxYmfSDoe0IIYS4fvZ08J5KqbeUUjOVUjOUUm9h38j/huTdxo2RfduxIz6Ts+eLDG/P1WIhaPpMLibEk79nl6Ft+Xn4Mqr9ULaf3k1mgYGzBYQQQlw3ezr4Z4GLwG+B3wEFwHNGBtXSTRgYSXk5fL8rrUna8x0Vi3v7CM7890vKLl82tK0JHcbg5mJmRbKM4oUQojmzp4OfpLX+vdZ6QOXPy1rri4ZH1oIF+XoyMCaE9fsyKCxycPra/HzMu3fWSlNrcnUl5L77KcnJITdupWPbq8PH3UJs5Ah2Z+7nZP4pQ9sSQghx7ezp4N9USv0/pVQPw6NpRSYNiqLocinr95903EHz82k7dhT+k8fiO250rU7eKzoGy4CBnF25nOKcHMe12YBxUaPxcPVgedL3hrYjhBDi2tnTwf8OmAtMV0p9opT6H6WUlIttRIcwH2I6+PPDrnRKSh2Tbc6s4/FIOgaA+4mjuCbUzkUffNc9UF7Oma+/ckh71ni7eTE2aiT7zxwi9Xy6oW0JIYS4NvYUm1motc7QWv8JeAt4DJD/q9th0uAoci9cYvuRTIccryoXPcCZgAg0gbXWuwUGETD5VvJ37aBQJzikTWvGRI7E2+zF0qQ4Q9sRQghxbexJdDNfKfWYUmoX8DWwCOhleGStQK9OAbQP9iZuR6pjprBV5aJfsYbFz/6bjVtOUXSxuNYm/hMnYw4IJOuLzygvLb3+Nq3wNLdhfIdYjuRojuclG9aOEEKIa2PPLfpZwHDg11rrblrrP2qtHfhgufUyVaavzT6ZTcJ/VzmmfrvFQtmAgQy/vR+XLhazbd2JWqtdPDwIvvseLqencW7D+utvz4ZREcPwcbew7ISM4oUQormxp4N/XGs9W2u91vBoWqEhHbz5+5fPE3PvFPwnxjqmkweCQi30HRRJ/P5TZKTVzg9v6T8QTxVN9iJj89R7uLozscMtJOYdR589Zlg7Qgghrp49z+AXNEEcrZbHUU14dsV8ePPRRMw6vpE97DdgeEd82nqwYVUipTVe5DOZTITce3+T5KkfET4YPw9flp6IMzyTnhBCCPvZM4IX16FExVDcteLFuMzQKEpUjMOO7ebuysiJ3cnNKWTf9tpJdarz1K/9kUvpxiXccXN1Y1LHsSSdT+FwjrEv9gkhhLCf1Q5eKfWwUsrYaik3AouFvNXr2PDuQp6868/os8WN73MVOnQJpEt0MLs3J3Mut7DWuqBpM3Hx8jI8T/3QdgMIbBPAMhnFCyFEs2FrBD9Ma52vlPp53RVKqYEGxtT6WCwM/p/pePj7smRzssMPP3xsV1zNLmyIO1qrg22qPPVmFzNTOo0jKzuFpDVfOew9AyGEENfOVgfvrZTqDAxVSkUqpaKqfoDHmyi+VsPDzZXJQzoQn5KLTs116LG9fTwYPLoz6cm5HD1SuwiM76hY3CMiDc9TP8jSjb/8fhGDZ/3coS8TCiGEuDa2qsJtBFYBoUBsnXX+wEO2DqyUCgPeAPpqreuN+JVSHwBdaizqA9ystU5WSiUDyZXLT2qt77fVVksR2y+cldtSWLwpiedn+Tv02D36haMPnWbzmmNEdQ6gjacbcCVPffpf3iQ3biWBt01zaLtV3BMT8U+vSJFb9TJhSX+50SOEEM5idQSvtX5Ha90d+JPWulPNH+B/7Tj2CGAxYLKyfrXWOlZrHQvcDqzTWidXrltQta61dO4A7m6uTBnSgYTUvGsfxTdQbAbAxcXE6ImqwbnxXira8Dz1NbPsnWofQEHXzoa0I4QQwj72TJP7M4BSKkAp5V9zWSP7LQQu2FhfM2H6/wD/qfH3KKXU80qp15VSwxprqyUZ3S8cX4s7izclXf3O+fn4jR+J/+Sx+E8YVa+TtzU3PviuewCMy1NfmWXv0Nef8fwfZ/DD2T3GtCOEEMIutm7RA6CU6gh8BQwEypVSO4H7tNbX0EM1eHwXYCIVBW2qvKi13qGU8gL2KKWmaq1tZlLx9/fCbHZ1REiGCQ72AeDucd15f9EhTp+7RO+uQfYf4MQROH4cAPOxYwRlpmDqNKTWJpOm9SIpMZvNPxzj0WdG42qu/A4X7EPJHTNI++Ir3DOn4turp0M+Uy3BPgR3msVNmwtYk7aB6b3H4ufp6/h2rkPVNRDOIeff+eQaOF9TXYNGO3jgT8BLwObKv0dULrvPQTFMA5Zpratf/9Za76j8t1AptY+KVLk2O/jcOlPEmpvgYB/OnKm4odG/SyD/tbjz8fLDPD/rZvsPEhKFf7fumI8mcjEygNPms1jO1L9JMnxcV1Z8fZDvlx+h/7AO1cs9RtyCOe4HEt99nw4vvYLJ1ZgvRBMjxrHz5H4+2vUds6LvMKSNa1HzGoimJ+ff+eQaOJ+jr4GtLwv2JLpJ01qv1loXVP7EARnXEkjlbf62dRY/CCyosc1YpdSkGuu7Asevpb3mquaz+ISUq3gWX3kb/OyKNSR/+Dty8zZTcimv3mbW5sZX5Km/1/A89SFeQYxsP4QtGTs4VeCYSnpCCCGujj0dfAelVHVdUqVUEBDZ2E5KqdHAbKCdUur3SilP4EVqTLFTSvUDErXWNR8mZwGPKKV+q5R6B/hGa73Jvo/TcsRe67N4i4XSAQPxi74LgJzUpQ0ml7E2N97Sf0CT5Kmf3HEcHq4eLDq2wrA2hBBCWGfPLfp/A0eUUplAORXT5hq9Pa+1Xg/UHSY+X2ebfcC+OssOAs3nvq5B3Myu3DqkA5//cJSElFyiO1zdtDmzux9+7ceTm7acgpw9WIL611pfNTd+4+qjHD2cSfdeYcCVPPUpr71M9uLvCL1/tsM+U00Wd28mdhjD4hMrScw9Tnf/Lo3vJIQQwmHseYv+R6AH8Fvgd0APqSznGFVv1C/alHRNKV4tgTfjYelI7snvKblc/1Z9j37hhIT7sHnN8Vp14yvy1N/CuXXG5qmPjRyBv4cf3x1bRll5WeM7CCGEcBi7is1orXO01ssqf84aHdSNomoUn5iWR0Jq/Q66MSaTicCo2wE428Ct+uq58UXFbF1b+zWGoGkzcPHyIvvjBbjuqj+v3hHcXd24rfNEUi+cZHfmfocfXwghhHVSTc7JRvcLx6/yWfy1jOLNHn74tR9H0YUkCnL21ltfNTc+4cBpMmp8iXC1WAieNJnu8/9FwJSxhqWXHRh2ExGWcJacWEVxqWML7QghhLBOOngnczO7cuvQjhWj+Kt5o74GS2D/ylv1qym5fK7e+qq68evjEiktuXKrPCAoGK/8iukajq5VX8XF5MKMrrdytiiX9Se3OPz4QgghGtZoB6+UavUvvDnbqL7t8PfxuOZRfMWt+tuA8gZv1VfVjc/LKWTf9tTq5aUxPSnu2AmAopBQh9aqryk6oBs9AhSrkn+koLh55ysQQojWwp4R/J+UUm9WVpYTBnAzV8yLT0w/d82jeLOHP37h4ym6cKLBW/XVc+O3pJB3trKTtVjI+3EzSc+9yN4BgynMOHk9H8Om6V2nUFRSxKrkNYa1IYQQ4gp7OvjfAO8ADymlFiilblVKWSsgI65R1Sj+Wt+oB7AE2b5Vf2VufOKVNiwWvJ5+FpewMDI//pCyYmNKyra3tGNIuwGsT99C9kVjCt4IIYS4wp5pct9qrdO11i8BS4GPgQNKqReUUpLU2EGqRvFH088Rf42j+MZu1VfNjT+ZksfRw1cyzLl4eBA6+0GKT5/m7PJl1/MxbJraeQIuJheWHF9lWBtCCCEq2PMM/iOl1K+VUieAOcBTwM1U1Ir/wOD4biij+oZf17N4qLpVP67iVv3ZffXWW5sb792zFz5DhnJ25XIunTTmVr2fhy9jo0axO2s/yedTG99BCCHENbPnFv0dQBdgutZ6pNb6c611MZAKhBka3Q3GzezCrUMrRvFHrI3irdSDr8kSNAAPSwdy0+vfqrc1Nz74nvtw8fQk8+MPKS8zJjHN+KjR+LhZ+Pbo8mv+EiOEEKJx9nTwv9BaP6a1PgCglAqvzCtfBjxnaHQ3oJF9bIzi8/NpO2E0/pPH4jsh1monfyUBThlnU5fVO461ufFmn7aE3HMfRcePcW7djw7+ZBXamNswpdN4jp9L4kD2EUPaEEIIYV8HX7eeaQzwmdb6XFVZV+E4bmYXpg7twLEGRvFmHY/HsaMAuB9LxDXB+rz1K7fqjzd4q75qbvyWJftx2bGj+suCz5BhePXoSfa3Cyk+a0zSwuHhgwj1Cmbx8RWUlpUa0oYQQtzorHbwSqkopVQU4Ff1e+XfV1n+TFytEVWj+I21R/ElKoaSbt0BOBXRke1B4TaPU/tW/fla69zcXRk9sj13vvMLAqeOq85kZzKZCJn9U8rLysj6/BNDbqO7urgyrcsUMgvPsOWUfEcUQggj2BrBV1WDm1nj9/XAMqD+RGvhMNWj+JPnOJJcYxRfWQ8+Z8UavvxgIUtzLpJ10fq0ttq36uu/Vd/pcibBZ9OB2pns3INDCLx9OgX79pK/Z5fDPx9An6AedPHtxPIT31NUUmRIG0IIcSOz2sFrrTtprTsBL1X9XvnTQ2v9ehPGeEMa0SecgLYNPIu3WCgbMJBpvTrj7uLCV8dPUWLjhbiKW/VjK2/V1y74UqJiKO7aDYCzwVFc6tK9ep3/+Il4RHUg6/NPKS0scOyHo+LLx8xut3KhOJ8fUutWFRZCCHG97JkH/3bdZUqp5xvaVjhOxRv1HTl28hyHk+s/C/dxM3NHpxBOXbzM6nTbiWMsQQPxsESRezKu9q16i4W81etJ+OA73rvnL2zbfaZ6lcnVldCfPETp+fNkL/zaYZ+rpo5to+gf0pc1qRvIu1Q/MY8QQohrZ+sZ/MdKqTCl1Ik6P0lU1IYXBhvRu13Do/hK0X4WhoT4sikzj6PnrI+yTSYTAVG3Q3kZZ9OW1bsjEHjbWLoP6cKBnemcrPFiX5uOHfEfP5FzG9ZRmKgd+tmq3N5lEqXlZSw/sdqQ4wshxI3K1gj+K+AssAEYU+dnkfGhiYpn8R05fvJ8g6N4gMmRQYR4urMwKZP84hLrx/IIqLhVf/5YvVv1AENju+Dr78na5QlcvnTlOIHTZmAOCjIsjW2QZyCjI4ax9dQuMvJPO/z4Qghxo7L1DH651voy8JjWOqXGTzIV+elFExjRp3IUv7HhUbybiwv3dA7jYkkZ3yZl2Xzr3eqteireqr9lajT5Fy6xec2x6uVNkcZ2UsextDG34bvjyw05vhBC3Ihs3aIfpZQaBQyq+r3GsneaLsQbm9m1chSfcZ7DSQ2P4tt5eTAxIpCEcwVsP2P9WXb1rfqyUs6m1c8kF9bel35Dokg4cJrko9nVy41OY+vt5sWkjrdwJEeTcPaow48vhBA3Ilu36L8AXgFebeBnkOGRiWoj+rQj0MazeIBhoX509/ViRWo2mRcvWT3WlVv1Ryk4e6De+oHDOxIY7M26lZqLhVduyRudxnZ0+2EEtPFn0bHllJUbkyZXCCFuJLY6+De01rdorcfU/QHeaKoARcUo/tZhFaP4Q1ZG8SaTiTs6heLh6sJXx09TbKMTtgQPwsM7ityTq+rdqnc1uzD2thguFZXUKitrdBpbN1c3bu88ibT8DHaeljQLQghxvWw9g3/Xxn6+BsQibBjRux2BbdvYHMX7uJm5s1MopxuZOmcymQjoUHGrPk9/g+uu2sVrAkMsDBrViRM6m6NHsq4c3+A0tv1D+xLl056lJ+K4XFrc+A5CCCGssmeaXJJMk3M+s6sLU4d14GxiCpl/excyMxvcTvl5MzTEl82ZeSTamDrn5hGAn98wOj74KgFTxlanqq3Sd1AkYe3bsnF1IvnnKzLNGZ3G1sXkwoyuU8m9lMe69E0OPbYQQtxo7Jkmtx6ZJtcsjAx14b0Fj9P7zRfxGdTPaic/KTKIUE93Fp6wPXXOL9sbz7SKkXjNVLVQUVb2lqnRlJWVs3aFru7M3YNDCJw2oyKN7W7Hp7Ht7t+FXoExxCWvJf+y4zPoCSHEjcKeaXIv1JwmB3gj0+ScwvPH7/G6fBGANhcLuLxiZYPbVU2dKyot45ukTKsj7dLoGEq6dgWgKCqEku7daq339fdi6JgupCfncnhvRvVy/3ETKtLYfvEppQWO74Snd50CBfnsXv4vm3XvhRBCWGdPudjf1fnbA/iXAbGIRlwaN5FyN3cA0v3DeaekE6VWXqYL8/JgUmQQ+lwh27KsTJ2zWMhdvYHTXy8g/u17OXtuW71Net4UTmQnf7auPU7e2UKgThrbbxyfxrZduTdzX17OnY++is/4kdLJCyHENbBnHnz7OnPg/QD3JotQXBEaSvaewxx84X95/p43OZRTyqKN1qv3Dg3xRfl6sTItm9OFVqbOWSy4jp6Jd9RI8s/soPBc7ZS0JpOJ2CnRuLi48OPyBMoqv1AYmcbWrOMJSD1V0c7x47UeHQghhLCPrRF81Zz3wdSeA/8cYEz1EdG40FBCn3mc0G6RuJtdWL41hf3HshvctGrqXBtXF746YXvqnF/4WNw823E2ZUm9qXMWHw9GTuhG5snz7NueVr3cqDS2Nevep4f7cSjY7LBjCyHEjcLWM/iqOe8v1JkHP0Vr/Z8mjFHUYTKZuGtMFy6XlOHr7c78ZUfIPnexwW0tbmbu7BxK5sXLrEqzMXXOxUxQx5mUl5eSk/wt5XWSzXTrEUKX6GB2bkwmO7PilrlhaWwr695nr1jNX//6c/6bsYbSslLHHV8IIW4A9pSL/azuMqXUU43tVznFbr5SaqeV9Q8qpbYppdZV/syuse4BpdRbSqn/p5R6tLG2bkTdIvy4qVsQFy+XUFJazr8WH6aktOERendfb4aF+rE1Kw+dZ2PqXJtAAiKncKkglXOnN9RaZzKZGDmhGx6eZtYsi6e0pKItw9LYWiyUDxjC1D4zySzMYv3JLY47thBC3AAa7eCVUtFKqRVKqaM15sG/bsexRwCLAZONbe7VWsdW/nxS2V4EFY8BntNaPw88rJTqZuMYN6w7RnehuKSM7pG+nMg4z3/XHrO67cSIQMIqq85dsDF1zjugD94BfTl/eiNFF5JrrfP0cid2suLsmQJ2brqyriqN7Zn57+G6a4dDX4rrFRhDTEB3ViR9z4XL8rKdEELYy5636F8CXuZK2difAp83tpPWeiFwoZHNnlRKPaeUelkpFVC5bCKwW2tdNbdrKzDZjjhvDPn5mHdXZJ4LD/JmZJ9wjiTnMrx3GD/sSmdXQlaDu7m5uHBP1EVvBgAAIABJREFUlzAuNTJ1DsA/YjJmjwByUr6jtKSw1rqOXYOI7hPGvu2pnE6veDvf7NOW0Nunoz76gIAp4+olzbkeJpOJO7vdxqXSyyw7EeeQYwohxI3AnreXUrTWu5RSFyrnwacopaY5oO31wHKt9Rml1BQqXtwbC4RQ+4vB+cplNvn7e2E2uzogLOMEB/tc3wHy8ykfNQZTgqY8OhrTzp38bFovtsdnYjK50D3KjwWrEugbE0p4kKV++/hwd1k5nx9J42BhEWM7Wj+tPl6zSdj+D/JPLafLTQ9hMl25ETPtnn68l76edSs1jz47GncPM0Ed2mHKr7hs5qOJBGelQqfB1/d5q+IO9mFSbiwrE9dye6+xdPSPvK5jCeeR8+98cg2cr6mugT0dfEellDfgrZSaTkV2u5HX27DWuub8rh+BJUopVyAL6FpjXVvA+r3nSrm5hY1t4lTBwT6cOdPYDQ3bzLt34p9QMSXNlJBA7qYdlPUfyPgBkSzbksxTd/TmP8vj+eMH2/ndT/rj1sAXnp5eHkT7erMw/iQhJhfCvDystNYWv/bjyU1fxYkja2gbUruzHj1JsfjzfSz9ej+jJnaH0A74de6C24njFAUEciE4Aq7z89Y0JnQUG5K28+8dXzDnpsdqfeGwlyOugbh2cv6dT66B8zn6Gtj6smDPLfrFVHTobwFvAguBudcSiFIqQCnVtvL3/1VKVX3B6AYkaa1LgTigv1Kq6v/gQ4GGU7bdYOpOH9sfWLF88uAoLJ5u/LArnf+ZGkNqVj6f/9BwXXWTycTMTiF4ml34spGpc5aggXj6dicv4wcuF56qtS48yo++AyM4vDeD1BNnwWIh74eNpP35r+wdMpyzWzY75kNX8nLzYmrniRzLS2LvmYMOPbYQQrRG9rxF/5XWepXWOkFrHa21DgHWNrafUmo0MBtop5T6vVLKE3gReLxyk9PAu0qp31JRvGZ2ZXvpwP8Bf1NKvQXM11o33FvdaCqnj51ZHsc/336K/6Qs4XRBFp4eZm4f3pH4lFxcXVyYMqQD6/dlsPXQ6YYPU1l1LuviZb7XKdXP9OsymUwERN2Oq9mL7ORvKCutnSxn0OhO+Ad6sW5FApeKivn/7J13eFTHuf8/W6Ut6r13HQmJKjqYjqnGFIMdsHFvKU5u8kuP4+Qm18m96U5xbxg3TLMpNtVgmuld6KDee19J28/vDwkZIQmECkjifJ5nn9WeOTNnZkdzvntm3nlfjEZcHnkc3egxVG7a0LtW9cCk4LGEGIPYmL4Vq6P39t3LyMjIDEYUnRlbtXit64zvi6K4rG+q1D3Ky+t7N7RZL9Pb0zLV5hr+ePwfGDQGfjz6u2gUWn71+lG0GhXPP5zCXz4+S05JHc8/PIYQX0OHZexIy2HmykUEFeRgj4unesc+MLZfuzebcilLX4Peayi+kYvbpJWX1LNxzSliEvyYtWgIAPa6OnJ//UvU3t6E/+J5FOrec1STXp3J30+/yoKo2cyPmn1TeeXpyduL/P3ffuQ+uP30wRR9p+uV13uC/xD4DW292F15je212sl0Cy9XTx5PfpDypgreS/0YpRKWTo2moNzEsUtlPL0oCVeNipc3X8Bi7dhJzFxTGUEFOUD7aHJX42qMwCNwCo3V5zBVnm2T5hfoRsrECNJTy8hsseBXu7vj/9DDWPJyqdy2pfcaDcR5xTDSfxg7c/dRba7p1bJlZGRkBhPXE/jfi6I44xovdle82/3+VlVQpnPivWJYGruQsxUX2ZHzJaMT/IkMdGPTgSyMOjVPLUqiuKKBNTvEDrfFSQlDsMQ2uxgoC4+iMU7o9FrugXfhYoygumA7NnNbj3gjJ4TjF+jGkS1nsR88DCYTbimjmx3gbNuCOadzf/ndYUnMfEBic+b2Xi1XRkZGZjBxPVe1L1/5WxAEP0EQlgqCsEQQBF9RFF+9NdWTuRHTQicxJmAU27J3klqZxvLpsVTVWdh9soAhkd7cOzmKIxdLOHCuuH1mo5G6nfs5/8l2fvfXNWwoa+h0f7xCocQnYgkKhZqKnA1Izm+c5ahUSmZND+OhN79P0NK5rfvg/Vc+iNrDg5I3X8dp7b01cx+dN7PCp3Ki9AwZNb3740FGRkZmsNAVT3azgHM0x4D/JXBOEISZfV0xma6hUChYmbCUUGMQ76R+iI+fg2ExPmw7nIupycbCSZEkRXmzdudl8ko7WPcxGgmcOplpcWGcrzZxsLTzaW+11h3viEXYmkqoKdrTJs23IhffqoLm81qm+1V6AwGPPI61uIjKTRt6td2zI6bj6eLB+suf4pQ63wkgIyMjc6fSlW1yjwGJoiiOEUVxNJAEPNm31ZK5GbQqLU8OXY1SoeS18++yaHIYTRY724/kolQoePKeIbjpNfxn8wUazR27qb0r0ItkLyNf5FeQWde5TwG9h4Cb3zjqy4+2CS179Ra+cu9Q0qTmPXyGpGQ8ps2gevdOGsW0Xmuzi0rLkpj55JuK+Lr4RK+VKyMjIzNY6IrA54mi2PpYJ4piNVDQd1WS6Q4+Om8eS1pFSUMZeyu2MWFoALtPFlBZa8Zdr+XpRUlU1Jh55/NLHU7DXwkt6+eq5cPMEmostk6v1RxaNrBtaNmWLXyV23az9Sevs3dfAWXFzWl+y+9H4+tHydtv4DR3HPWuO6QEjCDaI5LPMr+gyd575crIyMgMBroi8OGCINzT4qTGSxCEe4CQvq6YzM2T4B3H4tj5nC4/j29sEQoFbDqQBUB8mCf3TYvhhFjOnpMd/z5zUSl5MC4IhyTxfkZxp05wmkPLLkOS7FTmXhVa1mjEOWYsMx8Yg16v4YsNF2gwWVC6uBD42JPYKyspX/dRr7VXoVCwPG4RJlsDn2fvuXEGGRkZmTuIrgj8z4GfAhUtrx/T7LBGph8yM2wKKf7D2V24m1GjJI5cKGlde58zNowRsb58vDeDzKLaDvP7umpZERVAYaOFz3LLOzW607j64BU6H4spj7qSA23SdHotc5cNxWKx88XGC9jtDnRxcXjNmUftV/sxnTvbYZndIdw9lAlBo/my4CClDR0H2pGRkZG5E+mKwLuJojiZZp/wHqIoTmkJOiPTD1EoFKxKXE6wMZB09T507hbW789sTXt8YSJebi68svkCpqaOp+ETvYxMD/bmZEUdx8rrOr2W0Wc4eq9h1JZ8hdnU9l/CN8DIjAWJlBXV89UXl5EkCZ97l6ANCaX03bdx9GJI2Xti5qJVatiYsbXXypSRkZEZ6HRF4N8XBGEh0CiKohyQewDgotLy1NDVKAC3Iee4kFNGak4VAAZXDc8uTqa2wcqbW1NxdvKEPjPYG8FDz9a8MvJMna9ve4fNQ+3iRWXOxnahZWMS/Bg9KQLxQinnjheg1GgIfPxJHKZ6yj54r9fa6651Y17ULC5UpnGxsvcM+WRkZGQGMl0R+E2AAXhHEITfC4IQe6MMMrcfX50PjyatpN5ZhTH+Euu+zGgV86ggd+6fEcfZzEp270vr0Be9UqFgRXQgHloNH2QUU2/r2PpeqXLBN3IZDnsD1WnrUJ1oW9boyZFExfty5MtM8rKqcA2PwOeee6k/dpT648d6rb3TQifhr/NlQ/oW7M6O6yojIyNzJ9GVYDO/aQk4sxp4F/hQEIQbBpuRuf0M8RFYFD0Xh3shRYrzHLtU2po2Y1QIEyONTHl2GV7zZrY6p7kanVrFqtggmhxOPswoxuHs+Glfqw/Cy3MykQ//Bu/5bctSKBTMXJiAt6+BXZ9epKaqEe95C3CNiqZ07bvYa3rH3axaqWZZ3D2UNpbzVcHhXilTRkZGZiDTFUc3zwiCkCAIwp+Ar4AaQPZkN0CYHTGNEX5D0YSJfHL8KDZ7s8W7QqHg0UgnYdc4p7mWIL0LSyMDyDGZ2Z5f0el1PCsM6PKrOixLo1Uzd1kySqWSz9efx2qTCHzsCSSrldI1b3dqyHezJPkkMMRbYHvObuqt8mqSjIzMnU1Xpuj/BHwBNAITRFGcLYpi7+11kulTFAoFDyWuwEvrS1PgMbaf/EZ4VcnJWGKafdEX+oRS7B/ZYRnDfdyYFODJkbIaTld0bHTnSEjE3uLX3hzmQ1O4V5t0d08dc5YkUVdjZvdnqagDgvBdtpyGc2epO/hVL7S0ZS9/3EIsDitbsr7olTJlZGRkBipdEfi1QJQoii+IopjTx/WR6QNc1S48l/IoKhXsrNhEbUOL0ZzRSN2u/aR/tI1fPvZ3/rQ1nep6S4dlzA3zJcpNx6acMooazO1PMBqp3rmfii1buPzKM5SVfIbd2nYrXnC4J5Nnx5KXVcXR/Vl4zpiFLiGRso8+xFZR3ittDTQEMDV0IoeLjpNf37vx6GVkZGQGEl1Zg39WFMV+HWtd5sb46/1YErEMybWOfxxb+820uNGI54y7+M5D46lvsvHXdWc63D6nUij4VkwgBrWK9zOKabR3EILWaEQaNxWf5EdxSjbKMz/AaW/7YyBpZAhJI4M5czSf9NQyAh99HIUCSt56A6kTxzo3y/zI2Rg0ej65/FmvTf/LyMjIDDS68gQvM0iYETeKIOsISqV0Ps/c3yYtMtCd55YNo7SqkX+sP9thDHmjRs3K2CDqbA4+zizpdIudVuePX9QKbJZKyrPXITnbljVpVizBYR7s+1ykyqLF74GVNF0Wqdmzq1faqdfouCd6Dpm12ZwqO9crZcrIyMgMNGSBv8N4avy9OKsD2Jb7OenVmW3SEiO8eHpREllFdfx783nsjvZP1GFGVxZF+JFe18juwsp26VdwdYvCJ3wRFlMOlXltn6RVKiV3L0lCb3Rhx8YLqIaNxTBsOBUb12MtLuqVdk4MHkuIMYhNGduwOnovVK2MjIzMQKFbAi8IwrDerojMrSHAy8Akz7k4zXpeO/ce1ea229RSBH8enpvAhawq3tx2qcOn9DF+Hozxc2dfcTUXqzu3Vjd4D8MjaBqN1eepLd7XJk2n1zJvWTIWi50dmy7is+phFC4uFL/5OpKjg+n/m0SpULI87l6qLTXsytt/4wwyMjIygwx1ZwmCIKy+Tr4Hgbt7vzoyt4LFE+M58vYYzMIh3j32Bj/QTYLEoWA0AjBleDCmJhvr92VidNWwcnYcCoWiTRn3hPtR3Gjhk6wS/IaE46/Tdngt94C7sFtrqSs9gFrrgdF3VGuaj7+RmQsT2bHpIocOlzJm1UOUvPoyVdu34nPPvT1uZ5xXNKP8h7Erdx8Tgkbjh1uPy5SRkZEZKFzvCf4XwHRgOfACMLfl9QLQ80csmduGUadh/sghSBdiefa5l/BbMKedo5t548KZMzaMPacK2HIop10ZaqWSVbFBaJRK1mYUYe7kqVuhUOAdNh9Xtxiq8rfRVJfRJj1a8GPM5EguXywlyxmM29jxVG79DHNu+2t2h8UxCwCJbec3wtGj7Zz5yMjIyAxWrifwvxJF8VEgD0gQRXGlKIorgQQg4zr5ZAYAs0eHkVjiJLSoeYpenX4ZZVpqa7pCoWDF9FgmDQ1k88HsDkPMemg1fCsmkCqzjfVZpZ0a3SkUKnyj7kOjC6Aiez3WxpI26SmTIogWfPl6XyaWSfegMrpR8ubrOG09Xzv30Xkx13ccK578LYwf36HHPhkZGZnBSKcCL4ri+pY/fUVRtF113Ab49nXFZPoWrUbF6LtHY1M2r9IUBHvygeMCTukbwzqFQsEj8xIYEevLB7su83VqSbtyot31zAvzJbWmgUOZBR36tYdmn/V+Md9CqXKlPPODNnvkFQoFMxYk4u1nYM/OLFyXrcael0vj3/7SK2I8p8m/zQ+Zjjz2ycjIyAw2umJkpxYE4R+CICwVBGGJIAgvAZq+rphM3zPBzYqmJTDLsbFRHGhIY93lT9tavCuVPHNvEnFhnry59RLns9pbzk8M8GS0q5IJKxZ06tceQK1xwy9mJU5n+z3yGq2KecuGolQq2X/OQuLZc0T9+Q94TJvQY5FXJA6lKSYagPIwf2yC0KPyZGRkZAYCXRH4RwEr8EvgecACPNaXlZK5NTgTEmmMjAFg+vEaZgdN5EDhETZmbG0j8lqNiueWDSPE18C/N50no7CthzqFQsEScyVBBTnA9Z+StTp//KI73iPv5uHKnCVJ1Nda2DHqWzhRoM3LRTpyqGcNNRox7TrI15/8hx/+7h52VpzoWXkyMjIyA4BOreivIIpiHfDjW1AXmVuN0UjD3gN88NpWvrR48HzwNGwqBXvzD6BWqlkUPbfVel7vqua/7h/BH9ae5B+fnOWnq0YR6mdsLUqROARrbBzajHSKwyLJDowgrpPLXtkjX5m7mcq8LfhE3Nt6neBwT6aO9SPh7fdQIuFUKinc8TnBd01F6erao7aOW/YM+/e9ypasHUR7RBLnFd398mRkZGT6OV2JJhcnCMJ+QRC+EgRBLwjCFkEQIm9B3WRuBUYjkx67lyaNjvX7s7gvbhGTg8exM/dLvsjZ0+ZUD4OWH90/ArVayV8/PkNFTVObcmp37qd46y7efnUda4pruVBV3+llv9kjf47akn1t0pLdG/FriXKndDpRZmdT8tbrPXY7q1Ao+FbCMvx0Prx98X054pyMjMygpitT9L8B/hvIEEWxEXiC5ql6mUGCr6eOuWPDOZpaSnpBLfcLSxgXmMLW7J3syt3X5lw/Tx0/un8EVpuTP398htqGqyzdjUbUY8fx0Ig4QgyufJhZ0mn0OWjeI2/wGUldyQFMladbj9uFROyx8QBUeIdSv+QxTKdOUrVtS4/bqlO78ljygzTYm3jn4odtjAplZGRkBhNdEfgcURT30Lz2jiiKpUB1n9ZK5pYzf3wEXm4ufLD7MkgKHkxcTor/cDZnbmdffts18FA/Iz9YPpyaegt/W3eGRrO9TbqrWsWj8SFEuelYn13K0bK2a/ZX+GaPfDRVeVu/2SNvNFK9cx9ln+1kw3OvcLDMGyllKpWfbsJ07kyP2xrmFszyuEWkVaezI2dvj8uTkZGR6Y90ReCDBEHQARKAIAjh0OnyqswAxUWrYsX0WPJKTRw8X4xSoeThIQ8w3DeJT9I/5VDh0Tbnx4Z68O0lQyksb+CfG85huya6nItKycPxwcR76Pk0t4yDJR3/JmzeI78cjc6/7R55oxHF+PHc/eBY1FoVx5wCUlgsJa+/irWkuMftnRQ8jtEBI9iWvYvL1bJbBxkZmcFHVwR+DZAKLBUE4SJwEvj7jTIJghAoCMIbgiAc7yT9p4Ig/K3lfZ0gCAlXpeUIgrCv5fV+F9si00PGJvoTF+rBhv2ZNJptqJQqHk1exRAfgQ/FjRwtPtnm/GExPjy+IBExv4ZXPr2I45pwrxqlklWxwSR7GdmeX8HeoqoO19GVKhf8ojveI290d2X+fUMxm+2cC56JQ+1C0b9ewtHU1K6cm0GhUPAtYRn+el/euvgBtZbO7QVkZGRkBiJdiQe/DxgNPA78FBBEUfyyC2VPBj4FFJ2kG4EfiqL4v8AG4E9Xpb0jiuK0lteqLlxLphdQKBSsnBWPqdHGZy3uaTVKNU8mrybeK4b3Lq3jZOnZNnnGJwWyclYcp9MreH/TKVQn2jq6USsV3B8TyEgfN3YXVrKzoLJDkVdr3fGL+VaHe+T9At2Yfe8QKivNXB6+AktZGSVvvtbj+PGuahceT34Qs93COxc/kNfjZWRkBhVdiiYnimKlKIrbRFHcKopilSAIf+xCnvVAp49Foig+L4rilTu9ErjapHmKIAg/EQThd4IgTOxKHWV6h4hAN+4aHsyekwUUVTQAoFVpeHrYI0R7RPJO6oecLb/YJs+s0WEsHeXPil+uxnt+e0c3KoWCZVEBjPXzYH9JNVvzyjt0a6vVBeAXvRybuZLKi++hOvGN7/jIWF8mzYqloMRKwfiVNJw5TdXWz3rc3hBjECviF3O5JpPt2bt7XJ6MjIxMf+F60eS+BFYDubSsv7egaPn8s96ogCAIWuBh4DtXHf6ZKIrHBEHQA6cEQVgoiuJ1F0q9vPSo1areqFKf4ec3MKKZPbV0GCfFMjYeyOY3T45v3aP+/Mzv8T/7XuKti+/zk8nPMCIoqTXPIzEKFC1b29Tpl/EtzUURNb5NuU/4ueGRVsiu7DKUWjWrh4ajvCZKHX7D0Ttq0c9cgi6/CikhAcXx42A0MmNuIjaLg6NfZWMccw98thm/ZAGfcWO73LaO+mCR73TyzXl8kbOHlIghDAtM7HJ5MjfHQBkDgxm5D24/t6oPrufo5vtAIfAnURR/enWCIAj/2xsXbxH3l4FfiqKYeeW4KIrHWt4bBUE4A0ziBgFuqqsbe6NKfYafnxvl5QNnnfeeSVF8tCed3V/nMCL2m9ADTyU9wkunX+NPB1/h2WGPIXjHNicEROAVF486/TL53qF8nu5gcXRduzCz03zccVjs7C2opL7RwvKoQFTKtueoM+3o8qsAUKSlUXXgGI7RYwAYMT6c0uI6TmfAqKjRiH/5B+G/fB6X4JAbtul6fbA44h4ul+fw98Nv8vOxP8DTxaPL35VM1xhoY2AwIvfB7ae3++B6PxauF2zmnCiKzmvFvYX3ulMRQRC8BUFwb/lbB7wK/FUUxZOCICxrOT5TEIS5V2WLBTLblybTl8wYFUKQj56P9qRjs3+zNq3X6PnuiCfx0/nyyrm3yajJbk4wGqnesY/K7XvY9Me1bDlTwYb9We3W2xUKBbNCfJgb6sO5KhMfZBZjv2Yt3S4kYo9r3gffFOZNmT4TqWV9XKlUMOueIfgGuHHWZSgmvT9F/34JR2NDj9rrotLyRPKDWB1W3r74AQ6nHBFZRkZmYKPozDuYIAirr5PvQVEU775ewYIgTKV5in8uzU/pfwF+C1SJovhHQRA2AslAUUsWgyiKYwRBGEqzc52TQDBQKIriH27UkPLy+p65OetjBuIv5wtZlfx13VmWT4th3viINml11nr+fuoVai11fG/kk0S6h7emOSWJ93aI7D9TxIIJESydEt3uSR7gSGkNW/LKiXPXsyo2CK3qqt+bJhNq8RJVXlXU1B5F75mET+RiFIrmZZhGk4WNa05ht9gYlf4JPkIUwd/7AQpl52YlXemDo8UnWXPpY+ZEzGBRzNzrnitzcwzEMTDYkPvg9tMHT/CdGbJfV+DTgCOd5BsrimJSJ2m3BVng+4aX1p/jUl41f3hqPJ5GlzZpNZZa/nbyZRrsTXx/5NOEuQW3pjkliTVfiHx1toiFEyNYclfHIn+yvJaNOWVEGF1ZHR+Mq6q9HUVd6WFqinaj80jAN3IZCmXzOVXlDWxaewqd0s6Iix/gP28uvkuWddqWrvbB2kufcKT4ON8e/jhJPnLkud5ioI6BwYTcB7efWynw17Oi/7Uoio929AJe6LXayfRr7p8Zi8PhZMO+9qskni4ePDfyaVxVLvzzzGsUmb6JF69UKFg9V2DK8CC2Hs5l04HsDrfHpfh5cH90IHkNZt4SC2m0t58adw+YiFfIHJpq01oi0DV7zvP2MzBnSTL1FiWXEpZQsW0r9Sc7dLtwU6yIv5dgQyDvpn5Itbmmx+XJyMjI3A6utwa/7srfgiDMFwThz4Ig/EkQhHktW+Bk7gACvPTMHhPGoQslZBa1dznro/PiuZFPoVaoePXIv6g7tKt1a1uzyCe0iHwOmzsR+WE+bqyKCaK40cobaQWYbPZ257j5j8MrbAHmunTKsz7C6bQBEBrpxdS58ZRZ9WTE3E3xW29gKSzoUZu1Ki2PJz+IzWnnLXk9XkZGZoDSlWhyLwL/A7gAOuAPgiD8vq8rJtN/WDghEg+jlg93p3e4f91f78sP4lfxq5+sJWbJMoyzJ7cT+cnDgthyOIdPD2Z3eI1ELyOr44KotNh4La2AWmsHIu+bgnf4Isz1Wc3OcBzNgW4ShgUxamI4+Yog8ryGUfSvf+Aw9SxSXKDBn5XCMrJqc9iStaNHZcnIyMjcDrri6CYFSBFF8XuiKH4XGAWM6dtqyfQndC5q7psaQ1ZRHUculHR4TnB+JcGFzVvbdJlZZB/d3pqmVCh4ZF4Ck4cG8dmhHDYfyOqwjDgPA4/Eh1BvdfDOaZHGr4+0cZgDYPQZgU/EEiymPMoz38fpsAAw9q4oYof4k25MosDiRvHrr/TY092YwJFMCh7Hrrx9nK9I7VFZMjIyMreargh8hiiKrXfKlr9TAVos3mXuACYkBxIV5M76fZk0Wdo/XV+9ta00zI9/Wb7my/yDrelKhYJH5icwaWggnx3q/Ek+yk3HE6EefPfbK4lYNAe32VPbibzBeyi+kcuwNBRSlvEeTnsTCoWCGfMTCAz1IDVgCkWZZVRs7PlK0n1xiwgxBvFe6jqqzHIQRRkZmYFDVwTeRRCEdwRBeLLl9RagbdlGd8OgMzKDA6VCwcrZcdQ2WNl6JKf9CS374Ks/34Pjy+MkhI1gffpnrLv8aauPd6VCwaPzEpmUHMinB7P5rBORjyjIJjC/Oc01M52KM+1DxOq9huAbvRxrUymlGe/hsDeiUiuZtywZN08d58PnULjnAPXHjrbLezNoVRqeSH4Qh+TgrQvvY3e2/3EjIyMj0x/pisBPpNk17cSWlwLQA9Np3qcuc4cQE+zBpORAdh3Pp7Qjz4FGI/aUMWjcvXk8+UFmhk1hf8EhXj33LmZ781S6Uqng0fmJTEwOZPPBbLYcai/yV88GlIVH8RrupNW0X1PXewj4Rd+P3VxBWfoaHDYTrjoNC1YMReniwrmI+eSteQ9Lfl6P2u2v92Nlwn1k1+XxaebnPSpLRkZG5lbRFYGXt8vJtLJsWgwqlZKP91w/hrpSoWRp3ELuj1/Mxco0/n662SkONIv8Y/MTmZAUyKYD2Ww5nNM281WzAY279+Ph5cna9GJOVtS1u47OPRa/mAewW6ooTV+D3VaPh5eeucuGYlbpOR8wjfx//6vHRncpAcOZEjKBvfkH2gXbkZGRkemPdCVcbLuFTEEQvt+Stq4UU+L9AAAgAElEQVR9DpnBjKfRhUUTIzmTUcGFrMobnj8ldCLPDHuE0sZy/nTiXxSaioFmkX98QYvIf5XF1g5E3p4yBoOnJ08khBLtrmdDdin7i9vHlHd1i8YvdhUOWx1l6e9it9YSFOrBjIWJVGt8Oa9OpOSff0c6fLjdev7NsDTuHsLcQlh3ai2mw3t6VJaMjIxMX9OVbXLzBUE4KAhChiAIWYIgZNPsclbmDmXW6DD8vXR8uCcdu+PGlurJvon8cNSzOCUHfz35MpeqLgNXi3wAG7/KYtuRnA7zu6iUrI4LZpi3kR0FlWzPr2i3Xc/VGIF/7IM47A2Upr+L3VJNbKI/46ZGUWKMovJ4EYpJk/CaNaXbwqxRqnkicgm/+dkHRC1egvvdd8kiL3NnYjKhPnlc/v/v53Rliv4vNPuGn0Xzuvt0YHMf1kmmn6NRK3lgRhzFlY3sPVXYpTxhbiH8ePT38Hb15D9n3+Jw0THgisgPYXxSABv2dy7yaqWCFdGBTAzw5FBpDeuySrA724q8iyGUgNiHkBxmStPfxWauZOT4cIb62BiWuq+5nKwMlCeOdbfpBOaVElzYbE3vkpFJxen93S5LRqY/IkkSjWY7JVWNXM6v4URaGV+eKmDzgSze2yHy+gdHcY4fj9e8mXjNmSaLfD/meuFir5AqiuLuqw8IgvC7PqqPzABheKwPyVHefHowm/FJAbjrtTfM4+XqyQ9Tvs2bF9byftp6ypsquSd6DkqlkicWDAEJNuzPQqFQMP+a4DbQbIW/IMwXN42KHQWVNNqLWBUbhMtVQWq0+mD8Y1dTlrmWsvR38Y97iCnxGnyqvvFuV7lpIx4TJqF0cWl3jRtxxQBQnX6Z4hAf/tywnwcrokn2lWPIy/RvzFU1FB09RqZbENWShvoGK7WNVuoarNQ32qhtsFLfaKWuwdbpzJxRp2FYZQYBZc2Gq+r0y6jFS9hTZNco/ZFOg81cQRCEicBDwCnA0nL4htHkbjVysJlbT3FlA79+8xiThgbxyLyELudzOB18fHkzh4qOkuI/nIcSV6BRaXA4nbyx9RJHU0v51rgg5rmbsAuJYDS2K+NkeS2bcsoI0rvwcHwwRk3b36rWpjLKMpqjGvsHLyVg4TLUmRmUe4fy2aLvMdy7htDv/wCVTnfzDb8S6S4qlP9krqOgvogV8YuZEjrh5su6gxiMY6A/02Sxk15Qi5hXTVZ6Ed//6zOEVRWQ7x3KD1f+CbNWh0qpwN2gxV2vbXnX4Nb6WdMmzajToFYpwWTCa8401OmXscfFU71jX4djVKZj+kU0uSsIgrAdMAJZNG+XAzma3E0zWG9uH+1JZ9fxfH79yBgiAt26nE+SJHbn7Wdz5naiPSJ5eujDGLUGHE4n73xyglUvPEJYVcF1byBpNSY+zCzBXaPmUSEEbxdNm3SbuZKyjDVITjsBHvMIOpvKfk0sX52tw6upmNGaTKJ+8ANUPbg5me0W3r74Phcq05gZPoXFMfNRKrqy8nXnMVjHQH/BYnWQXliDmFdDWm412cX1OCUJlVLBNFsBP/jHd1rPTf9oG9qJ49G5qDuM8nhDWn7kdvYDXKZz+pvAHxRFcfI1x+aJotivNgTLAn97aDTb+PlrXxPgrefnq0bd9M3iZOlZ1lz6GC8XD749/DH89X4oTxzDZ/6s1nPKtuxCMW5ch/lz65tYk16EWqngkfgQgvRtp93tlmrKz79O/DMv45pXiT0unmN//5g9e/MxWKoZ4zhP7A+fQ+3hefONb8HhdLA+/TO+KjzCSL+hrB7yAFqV5sYZ7zAG6xi4XVhtDjIKa0nLqyEtr5rsojoczmZBjwxyIyHci4QIL2KDPXCxNslP3f2E/ibwvwI+FEUx86pj3xNF8Z+9VsNeQBb428f+M4W8+4XI04uSGDck4KbzZ9Xm8Oq5d5GQeGrow8Sq/VpvRvneofz1+y/z+P1jCPDWd5i/tMnCO2IRZqeTh2KDiHZve57i6y/xXXRv6+eq7bvJ8Ynli/XnUVtMjDafJOG/voPGx+em634FSZLYm3+ATRnbiHQP5+lhD+OmlW+gVzOYx8CtwGZ3kFlYR1peNWl5NWQV1WJ3SCgUEBnoTkKEJ4nhXsSGeuCq7cC8ymTCryyPcv9wWdxvI/1N4LNp9lhXQfMavALwEkWx+488fYAs8LcPp1Piv989jrWmjv+Z4IYiOemmbyDljZX859ybVDVV89CQ+xltiEUtXuKs1p9X9uTicEo8Nj+R0Qn+Heavsdh4+3IR1RYbK6IDSfa+6vomE153T0WdkU5TmDfZb/0Cr8QHqK5SsO2j09gamxhlOsHQ7z+ONiCwJ18Fp8vO827qh3i0zEgE6P16VN5gYjCPgb4iJ6OY4q+OcVThQ2qFHbvDiUIB4QFuJIZ7IYR7Eh/mic6lK/bSch/0B/qbwB8AHrw6D/AbURQf6ZXa9RKywN9eMsRCQu+dfcN18+thsjXw2rk1ZNZmszRwKnc3+uNISKTSoeblTy+QVVTH7NFhLJ8e02zscw2NdgdrLheR32BmUYQf4/yv+g3a8vSSo66lqnofCqUGn/BF2KQwtn5wivpaM0PrjpPynVW4hIb16LvIrs3llXPvIEkSTw17mFjPqB6VN1gY7GOgN3E6JT794jzzfvgAYVUFlPiFs/n/3ic6PhghzBO9a/eWgOQ+uP3cSoHvijXQHFEUc6965QB/7rXayQwKEkxFhLVsRbuydeZmMWoMfG/kk0xwT2Dyymfxnj8TzzlT8VHZ+dmqUcxKCWXXiXz+9/1TVNWZ2+XXq1U8JoQQ76Hn09xy9hRWfuP1zmiEceMwhE8gUHgSlcaN8qyPcJgOsOSRFHz9DZx1H8ehf3+COafjIDhdJcojgv+X8l0MWj3/PP0aJ0vbB8uRkemMJoudf208j7jjcOuYCizP4/5AKyPj/Lot7jJ3Hp0KvCAIQwRBUAD3CYKw+uoX8NdbV0WZgYBdSMQaEwdAWUA4dqHr2+auRqNU8zBDCS2qaf6cnk7dmcOoVUpWzo7n2cXJFFY08Ju3j3O+A1e5WpWSB2ODGeXrxp6iKj7NLW/n9U7j6ktg/OMYfcdQX/41tflrWXB/LOER7ogeo/jytc9pENO6Vf8r+Ol9+FHKd4hwD+Otix+wM/fLdi52ZWSupaK2iT+sPcXZzAomLJ/eGnTJHhffbLEuc0tpqqnAeaxnLq5vJ51O0bdMza8EdgDXxtyUt8ndJHfE1JjJxOnN+3k9Cx5ZPpqxiTdvcHelnCtGdkUh3jz/hxUsHr6CcYEpKBQKSqoa+c+mCxSWm1gwMZLFk6NQKtvOUkmSxM6CSvaXVJPkZeD+ACNB5QXtDIwaa9KoyvsMSXLiFbqAE4fgUmolgQ3ZzFyegvuwYT35RrA5bLx3aR0ny84yKXgc98cvRqVU9ajMgcodMQZ6QEZhLf/acA6bQ+LZxUkkR/n0+nY0uQ/a4pSc1FhqqWiqpKKpivKmSipb3k1VJfzqp2sJLarp1Z0H/W0NfqkoihtvdOx2Iwt8/8DplPjduyeoabDw4pPju2z8046WG1tlVAhv53xGek0WYwJGcr+wBJ3aFavNwdpdlzl4rpjECC+eWpSEh6G9N71DJdXsvpzHb374ML55WR0OVLu1loqcDVgbCjB4jyArK5YTR8vwbipm1oI4fMaO7ua30YxTcrI1ayc7cvcyxFvg8eRVuKpde1TmQOROGQPd4ciFEt7+/BLebq48d98wgn0NfXKdO6IPrvlRZHFYW0X7ipBXNFVSYa6kqqkau+RozapUKPFx9cJX50NSVg0rnvnv1rTqz/f0ise+fiXwAwVZ4PsP2cV1/P7dE8xMCWXl7Pgel+eUnOzI2cu27F346Lx5LGklEe7NhnAHzxWzdqeIzlXNs/cmEx/WfnNH1t59jHtgUevnjgaqJDmpLd5HXelB1K6+1DVMZN+uKgzWGu6eGkDAtEk9bsehoqN8JG4iyBDAt4c/hqeLR4/LHEjcSWOgqzgliU1fZbHtSC5CmCffWToUo67v1tgHax9IkkS1pYaS0kyGL38Iz5xCysL8+O0fV1KusrQ5V6d2xVfn0/xy9cZP54OPrvnd08Xjmxm2PvLYJwt8N5AFvn/x3g6RfWcK+fXDN+fh7npk1GTzzsUPqbPWsyhmLjPC7kKpUFJQZuLfmy9QXt3EsqnRzBkXjvJqhzsmE26zp+KamU5xaCS73v+UmfERaDuwxDfXZVGRuwnJYcGpHs+OrU40NjMzxxgInzu9x21IrRR548J76NQ6vj38MUKMQT0uc6Bwp42BG2GxOnhjayonL5czZXgQD94tdLg7pDcZDH1gddgobiih0FRMgamYQlMRhaYSmuxNxKaX8uLzm1rP/ejlX2JLGYOfzrtV1PVqXdcdcvWBxz5Z4LuBLPD9i0azjV+89jU+Hjp++VBKuzXy7tJga+T9S59wtuIiQ7wFVg+5HzetkSaLnXc+T+N4WhkjYn15fGEihqutjU0mPIpz+Mhu5IDJhq+rhuVRgYQZ20+VO2wmKnM3Y67PQukSy57tXlgtCqYmKIhfNqfHbSioL+Llc29jtpt5MmopyeX2O8Ll5502Bq5HVZ2ZlzacI7/MxP3TY5k9Jqx7LmNvkoHUB5IkUWuto6C+iCJTCQWmIgpNxZQ2liO1eE3XqrSEGIIIcQsi1BhEmMKTEfc9iCYjvd967JMFvhvIAt//OHKhhNe3prJ6jsC0kSG9Vq4kSRwoPMKGjK3o1ToeHvIACd5xSJLEnpMFfLw3Ay83F55dnExUkHtrvit9kFHXyIasUuptdqYFezM9yBtVB0Z69WWHqSn6EoXKyInDYZRVeTA+3MywVQt6fDOuNtfw5tFXeO77/yGkl414+it34hjoiKyiOv654RwWm4OnFyUxPNb3ll271/qgl55sr+iPw1RL7ZkjZAUbyXPWtD6ZN9gaW8/1dvUixNgs5CHGYEKMQfjqvNvHfujnfvJlge8GssD3PyRJ4k8fniav1MSLT43HvQMjuJ5QaCrmrQvvU9pYzuyIaSyMuhuVUkVmUS2vbL5AbYOVB2bGMX1kCAqFok0fNNkdbM0r53RlPSF6F5ZHB+Kva18/S0MBFTkbcFjryckI4WJGOKN1xUzydWKdPQcCurlTAHAcO0Tgwnmtn9e/+gKRM1cQ7hZ6S57mbjV34hi4lmOXSnlz2yU8DFqeu28YoX63UIBu4KrWKTlptDdhsjZgsjVQbzVhspla3hswWZuPWeuq+MF/vUpwYRWFIV786sX7sLhqWyORSXxzK75WX65Ou4KL2cYffrGB0KIaCoI9+fUfV+DjE0GIMbBVyEOMQeg13Yj82A+RBb4byALfP7kSUnbckACeWDik18u3OKysv/wZh4uPEeUezqNJK/HReWNqsvHG1lTOZVYyNtGfR+4KJaymqN3N7UJVPZtzy7A6JOaE+jAhwLPt+j3gtJupzN9CU80l6iqMDPn5uwTkZePUaKk8dbH7In+VEU9FeAA//v29NGiV+Ot8GR04kjEBI/HX37qnu77mTh0D0Cx0nx7M5rNDOcSFevCdpUNx1/fuD97OcEpOqisLCFu4EENWDjWRIax77QWq1Q7qbQ2YrCbqbSYabI04pY7jwOvUOtw0BoxaI0JGOY9+7y+taZtf/x0VyW2NaRUoWt4BxVV/X/XXlc++F9NZ+PjPW1Mrt+/GOXpsD1vdf5EFvhvIAt9/2bA/k21HcvnpypEI4V59co2TpWf4IG0jCgWsTLiPUf7DcEoSn3+dy/bdF/nHRz8hqDy/w6nwepudTdllpNU2EO2m476oADyvCT0rSRKmypPUpW0m4btrcG1xxHP6mV8T/Jsfdd/G4KrpxEYXFWfKz3O85DTpNVlISES6hzMmYCQpAcMHfPCaO3UMWG0O3tx2ieNpZUxKDmT13AQ06t43prM77ZQ3VVLSUEZJQykljWWUNJRR2lhGhFjYxvjsty8+QHlSLEatsUW4DbhpjBi1RowaA24t70atAaPGgFp51XbX3rYuv8Piy8sC3w1kge+/WGwOnn/jKBq1kt8+NrbPLIUrmqp4++IH5NTlMSl4LPfFLUKr0lK4bS8jHl3cel7u+s/RT2m77U2SJE5W1LE1rxyFQsE94X6M9HFrN1Uufb4O/4efaP287uFfURI6mrvuHUFopHevtaXaXMPJsrMcKzlFoakYpUJJglccYwJHMsw3CVe1y40L6WfciWOgut7CvzaeI6e4nvumxTB3XHiPl1+sDiuljeUUN5RS2lBGSWMZxQ1llDdVtHkC93H1IsDgT5A+gBCFGzMffA5dVjb2uDiqd+zvsSj36jp3P183700GhcALghAI/B4YLopiO+8AgiC40uzTvhCIA/4oiuLllrQHgZGAA8gURfHVG11PFvj+zdmMCv6x/hzLpkazYEJkn13H4XSwNXsnu3L3EWDw57GklYTghufdU9FkpFPgHcpPV/+FaXcJzBsXgYu2rVe5KouN9Vkl5JjMDPE0sDjSH6PmmqeXmZNRZ2dhDvMh9aVVFFYGcikrloCgACbOFvDsJKxtdykylXC89DTHS05TbalBq9QwzC+JMQEjSfSOHzCe8e60MZBbUs9LG87RaLbz1D1DGBl/48iCTsmJxWHFbDfTZDdjrq3EcuEk6UF6CqQ6ShrKqDJXt65lKxVK/HQ+BBoCCNT7E2hofgXo/XFRXbMEIIeL7RcMFoG/j+bwsi+IotjOFZggCD8DnKIo/p8gCEOB/4iieJcgCKHAVmCkKIqSIAjHgZWiKKZf73qywPd//rXxPBeyKvn9E+Pw9exbg5lLVZd5N/UjzHYzS2PvYYo6Cr9jXyEmjmNdaj3HLpXh5ebCsqnRjE8KbLPu7pQkDpXUsLOwEleVkqWR/iR6tQ0/qxYvYYuNosZ0mvrSr3E6JHJyg8nMjSRxZCQpEyNw6eWgIE7JSVZtLsdLTnGq7ByN9iaMGgMpAcMZEzCSSPdwFA0N/fZJ6E4YA/a6ahrPn+Co2siGM4XodBKzxgahN/CNaDta3u2Wdp8tDkureF9tfFYY4sVLf38Wb58wggwBzU/mhgD8dD5tp89vwJ3QB/2dQSHwAIIgTAP+3InAHwB+IYrigZbPdUAosByYKIri4y3HXwIyRFF86XrXkgW+/1NVZ+aXrx8lMcKL5+7rmY/3rlBnrWdN6sdkFV3kb7/eim9eaesaX3qNnY/2pJNdXE9koBvfmhVHXGhbL3gljRY+yS6luNFCiq87C8J9cVW1f1q2W2uoytiG2ZKJ3aJAzI6itDycMXfFkDg8CKWyb9ZbL1aKHC89zYWKVGxOOyG48cJPP8AztxBbbBw1O3s4DdvLDKYx0GRvalnrLmtd666uLOD//fB1QlqswX/+4jIs1/zI06q06FQuuKpdcVW7olO5tvztgk7tiqvKtfld7UJgahbjVz7dmrc3XKUOpj4YqNxKge+mo/BewR+4upV1Lcc6O35dvLz0qNX9e6rSz693PLoNVPz83Fg5J4G3t14ks9TE+OS+9eLmhxsvBH+fQ+v+jW/em0BzKFuPokwmTprM+OGh7D9dwLvbUvnD2lNMHh7MIwuTCGiZYvfDjcQwb7akF/N5Zik5DWYeHRaB4HNtP7oRFPIM9RXZZB16h6SELGLCC0g9W0La2WjuvjeZ6C5Mz94sQQHjmTVkPI3WJo4WnCbri0/wzC0EQJORzrotv8N10hTifKKI94kiwOh327ffDaQxIEkSteY6CupKKKwrIa+miNzqIopMpZhs39yiFJIKtd1I7IVqQlqML0OLavi121S0M6aj0+jQa5qF+6aWU2JMkPA3SEuDhAS8Jo/tNVepMreXW9UHt1Pgy4CrW+necqwMiL3meMaNCquubrzRKbcV+ZdzMxMS/dh51MArG84S6qVrtwbeFySMvQ9zzN9xzcyiINiTP+ZuYLbRyvigFJLDPfn94+P44lgenx/N5esLJcwZG8b88RGtgXIme7sTrtXwSVYpfzmazlQ3DXebyiBhyDU3XF+CJvyI6jM7cZoPMmp4GnV1BWxfV4CbTwwTZ8T0+vr8FZLdhpI8Lwpr7Fa0GRnURARTGR7C5ewj7MjYD4BRYyDSPZwoj3Ai3cOJcA9DdwuD3vTXMSCVFFO3fRPnhyWQrbJQ2lBKla0Sk7Mah8L6zXkOFVKTEWeTG5I5CGeTEa3dHQ8XL7zdXIl3nm9Trq/CG7vNA2zQhJMmunGP2r73myWXJgmaevb99dc+uJPogyf4TtNu6RS9IAjegF0UxTp5Df7OJb2ghj+sPcW8ceEsnx574wy9QYuB0VEXiY3FX5Jdl0eQIYDFMfNJ8klAoVBQVWdmw/4sjlwswd2gZemUaCYPDWrdAmd1ONkl5jLnwXsJKsjBFB1L4+79KIztB5i1qpKS7a/iDDejdFNTXuFN2uVoIhPiGT2p99fnr27n1WvwTslJcUMp2bW5ZNflkVObR0ljGdC8VznIENAq+lEeEQTo/dp7BuslbvUYkCQJs8NMraWOGktdy3sttdZvPpsqSvjlz9a0mVY3qwxITQbUdnf0eOGp9sFf54e/0Qtvd1c83VzwMrrg5ebSNlqiyYTX7CmoMzOwx8RSveurfrVEAvJ9qD8wKNbgBUGYCqwG5gIvA38BfgtUiaL4R0EQdDRb0RfT/MT+4jVW9KNptqK/LFvRDz7e2n6JIxdKeOHRMbfMm9eVPpAkiTPlF/g0czvlTZXEeUazJHZBa4S6rKI6PtqTTkZhLWH+Rh6YGUdiRPP+ffXJ43jNm9la5n/+tZb4mdMZ6m1s5yBHcjqp+mIrNTl7Uad4gUZBfmEgeQUxDBuXwJARfbM+fyMabU3k1uWTXZdLdm0eOXV5NNqbAHBVuRLpHkaURzgxKl/CC2vQJI9E697zLYC9OQbs9TVYzp+kLCKIao29WbivCPgVMbfWYXVY2+XVqXV4aN2wm10IOpDK8/98vzXt4q9fRPXQE3gYtd3bztnPt3vJ96Hbz6AQ+FuNLPADi/pGK7947WtCfA38dNWo2xJow+F0cLDoKNuzd2GyNZDiP5xFMXPx1fkgSRLH08r45MtMKuvMjIzzZcWMWAI0zlanHA3Rsfz5n+9TgJpAnZaZIT4M8TS0a0tTViYl776CFOVENcwTSVKQmR1CVZ3AxHFhRJhLbqsgOCUn5Y0VZNfltT7lV1Tk8eIv1l/lPvR+NG5euGkNGDXGqxyjNH92a3GIciXNVeXS9nvoZIuWJElYnTYabA002BoxXfPe/GpofTfZGrHXVfObn33UWrcrxmxqpRpPrTseLu54unjg4dLyd8sxj5ZjJpOTN7elkpZXw10BSn78q2UobDYkjZaKnngmHADI96Hbjyzw3UAW+IHHV2eLeOfzNB6bn8jkYX0fNrWzPmiym9mdt589eV/hlJxMCZnA3MiZGLUGbHYHO4/ns/VILna7k1mjQ1kUq8fzwB4ss+bg9PfnfJWJPUWVVJhtBOtdmB3iQ7yHvo3AOZqaKHt/DabU47jMioAgCZtVTcA7h4jYfBBbaBR1Xx7sN099zmOHCVg4t/Xzptf/m6y4IOptplZf5SarCavT1mF+tVLd7BFNY8DboeGZ7/0Dn7wSKsID+M9Lz1GttreKts1p77QeOrUOg0aPUWPAoNFj0OiJvlzM0qdeaD3n8sZ1aMdNuWEYUEmS+Dq1lLU7L+OUJFbOimPy0CAUZWW47N6BZVbPYgsMBOT70O1HFvhuIAv8wMMpSfxh7UlKq5p48anxGHV9tC7dwo36oMZSy7asXRwpPo6LyoU5EdOZFjYZrUpDrcnCpgNZHD+exd8+/DEhlQXYYuOp2bkPjEYcksTZynr2FFVSbbETZnBldogPMe5tRafuyCFK176H0l+LcYIfMT9/E11+FXaDC3uf+TMRj9+Ht6+hT7+HLtFF96EWhxVTSzCS1qAkVwUmMdlMeJ5P47kfvtKa599/e5aqoQkYWkTbqDZg0OoxaAytQm7UGNCrdR1bnXfDtWmD2cZ7O0SOXSojNtSDJxYOwb+PfTH0R+T70O1HFvhuIAv8wCS/zMRv3z7O5GFBPDIvoU+v1dU+KG4o5dPM7ZyvuISniwcLo+cwLnAUSoWSil37SVx1T+u5X/zlA5IfmIeLplmIHM5ml7dfFlVRa7MT6aZjdogPUW7fiIm1tJTi11/B5cQxhh4+0Hr8zP89zBnNBFzdh5Iyqe8s7rtMb60n94Wv8ZuoW2pOFW9uu0Rdg5V7J0cxf3xE92MHDHDk+9DtRxb4biAL/MDloz3p7Dyezy8eSiE2xKPPrnOzfZBencmmjO3k1ucTbAhkcewChmiD8Z47HXX6ZUoDwvnu8v9F6+nOnLHhTB8Vgqu22ara5nRyvLyO/cVV1NscxLrrmR3iQ5ixeVuaZLdTteZt4n/+/1BKEnaDK5fW/QqrugazRUtWdiiuniMYNSEG98HwpHkb3KTa7A427M9i5/F8Ar31PLVoCJGB7rfk2v0V+T50+5EFvhvIAj9wabLY+dUbRzHqNPz6kdGo+siyvDt9IEkSp8rO8Vnm51SYqxC8YlnqOY6Yw2exzJpDus2FLYdyuJBdhVGnYfaYMGaOCkXv2iz0VoeTo+W17C+uptHuIMHDwKwQb4INru0s8s/Nnoti5TwcEXYctmIsVg05uaG4eI5i1PgYjO63bs96X3Arx0BeaT2vb02lsLyBGaNCWD49tnWW5U5Gvg/dfmSB7waywA9sTqSV8Z/NF3hgRix3jw3vk2v0pA/sTjsHCr9m76Xt/OonawktqqEhOpKG3QdRGN3JKqpjy6FszmZWondRM2t0KLPHhGFo2e9ucTg5UlrDgZJqmhxOkrwMzPTUkXjvbNTpl7FFRJK26mHqLl4AwDB1GFKSHiel2GxqcvNDcPVMYcS4OPTGgRdJDm7NGHA6JXYcz2PTV1kYXDU8tiCRodE+fXrNgYR8H7r9yALfDURrsjwAACAASURBVGSBH9hIksTfPznH5YIa/ueJcXj3wdNqb/SB49hBAhfOb/38x/9bTfDUxUwIHoOniwe5JfVsOZzDqcvluGpVzEwJ5e4xYbjpmyN7me0ODpbWcKikBqvTySi1g3kXj6OffTcEBGCrrKDmy73UHtiPs6EBbXI4yvEB4FKJ3a4iryAYnddYho2NQ6fXdlbNfklfj4GK2ibe3HoJMb+GlHg/Vs8VWr93mWbk+9DtRxb4biAL/MCnrKaJ5984yvAYH769ZGivl98rfXCVwZgpKpy//e1pzpvzUaAgySeBScFjSfJJoKiiia2HcziRVoZWo2L6qBDmjA3Hw9AsOI12B0cyC5i28h4C83OoiogmZ8tuwgJ9AXBardQfPUL1nt1YC/JRhXigmh6Fwr0Op1NJQVEwOq9xDB0T33de8XqZvhoD32x/E3FKsGpWPJOGBt52v/v9Efk+dPuRBb4byAI/ONhyKJtNB7L5rxXDe31qtdf64BoL7vLGSg4XH+Pr4hPUWevx0LozIWg0E4LHYmlwYdvhHI5eKkWjUjJtZAhzx4XjaXRptwb/4l/ewZEyhsmBngzxavaMJ0kSTZdFavbuxnTqJApPDaqZcaj8zTglKCoOQu89nqSUBLQutzO0xI3pizEgb3+7OeT70O1HFvhuIAv84MBmd/LCW8dwOJ387vFxaHvRMKqv+8DhdHCh8hKHio6RWikCIHjFMilkHAHKSL74uoAjF0tRKhVMHR7MvKE+xNw3p3kNPjaene9/ylf1NqosNrxdNEwK8CTF1x1ti8vUq6fvJaUFpkSiCZcAiZKyQHTeE0gO8UG/f3e/dNrSq9+/yUT+vqO8nC5RblPd8dvfuop8H7r9yALfDWSBHzxcyqnin2uOsDLYzuQHZvbatqpb2QfV5hoOFx/nSNFxqi01GDUGxgWlkGAczrHTJg6dLwFgRpSexfWX0C2YBwEBOCWJ1GoTB0tqyGswo1MpGevvwQR/T9xbtuC1mb6vLsI5PhTXODUqi4WkJ9/GpaIeh0bD2fUHcU+IwM1D1y+Er7e+f3NVDYYZU/AuyqHYL4yiLbsJj+57T4iDAfk+dHM4y8pQZ6bjHDq8396HZIHvB8gD6yYwmVBMnIBvSS6NkbE07O2dqFy3ow+ckpNLVf+/vTuPr6OsFz/+mfUsOSfJydKs3ZcpFGgpiyD77gIuiFQF9CrXn3r14lX0h8u9XkQUFxBcUF6iePUnCnhlB9lEUUApWCgF2mnSpknTJE3TnCRnnzkz8/tjJidpmpYuadKmz/v1mtfMPHPOnOfMnDnfeZ555pn1PNe1kjV9b+B6Lgsq53JM5bH0rFG45JqP0NzfSU/tLB688bcsOqKZRc0VaKpCeyrHs1uTvJHMIEuwtDrOqXUJ6qN+K/odqu/XvkxFLM9RP3+o9NmvfP3DrK9bTnIggSc3UFFdTlVNGYnqMqpqo5Me+Pd3+7uex99f6+HlOx/h63d8vpSe/OOfKB53wkRkcdoT/0M78jwPZ2gQu7cXq7cXe1svdjAubunk6MceIZpOTVwHTUxugD+4L9oJhyXVXEuipx2A6KZWOl9YReKc06c4V/tGlmSWVC9mSfViBgspXuh+iee6V3LvpnuJxjWq3tbMuU8P0dDTQcsTz/PQ6kVoqsyimZUsmVPFGXOruKCpmud7B/ln3xCr+lIsLI9yan0lC8qjRI3FRI3F2Nv7sH94Cx4gAXZFhGJNmHmzO5HmduK6r5FKxdm2pYLWNZUkB8qRZI1EVZREbXT8wH8QPRltQ9cgv3uqhY1dQxjzDHJzFxBpa6W4cJGfP+HQNpG/tTHr8lyXYv927G3bsHq3+gG8txdrmx/IvUJh5L2ShFZdgzZjBjWNTUTTfiBWW9b76zzETiRFCX6SiDPnvTCqpfqW6plc+39+wBeuPHW/b507WPaB67m0JDey0nyaFZ+6nqauAbrrK7jrtq8Rii4k3VdBa1uB7u1ZACrKdI6cU8XCWZXkYgqvpDKkbIf6iM6p9QmOqYqjBgE5cc6pqG0bsRoa2fTf15Pr34aV78KNZJEbQ0gzQkiyhOdALhVlYKiKrf3V9PTGcT3/Wr+iytSWSay47d+o6G6nMHcBg089gxTf+bn3e2Nftn8yVeAPz2zg+dd6qIjpvP/M+Zy0pB45kzloTj4OBZ7r4nRuJtG5kWTzPEgkkPUQkq7v290GexmQPdfFswq4+TxuLoeTy+Pmc7i5HF6yn+YvfJZQ1xYKjU10XHcDXjgCeOwUnzwPSkneSNqwXI451/0Xoe4u8tU1vHbReykMDoDjlF4iqSpaTS3ajBnBUIdeG0xX1yCpauk7TngXy4gq+n0iAvw0E/yBbKqayQ33myTiYb502fL9eiDNwbYPxraiv/6GD/HqXL8r1froDObE5qLlZrC9qwxzU4Z0zn9yW3NtGTV1MQbLZLIRmYqwxsl1lZxYW0Fke9+4T0bzikWsrT3kOzeR72/BKvbgRvOQkJEkCa/oYm+DbKqcwVw9Ussg7739mtL7f/Xhm/BOfAuNsyppnFVJTV0MZS+fl74327/0FL/n23FclwtOnMU7TppN5CC/U2CqeY7jl1S7t2B1d1Po8sfFze0c89QTRNMpsrE4q08/CzcIZJKuI4dCo8Yh5LFpIT9N0kMonseca7+K3rWFQmMjbV/6Gg5eKXiPjHO4w4E8n98xEI8SS/az9G9/Kc2vPu1M0omqffr+Y9fV8vFPYR97HNqMGUEQr0NNJJD2tLfMia7FOgBdNosAfxA42ILLoWRde5Lv3/MKcxrKuXrFsn3ucvSg2wdjSgj9jz9Np5fGTLawrr+F1oE2bNdGlmRmx5tp0GfjpWro6tBo7UxRdDwURSJeFcGp0EjEJL7xnx+jqmMj9vwFDDz55m0X7HSSTOdq8gMbsL1teGELAGkwz5JP/4rQthTFshAPfvRzbAkvJOnWABKKVyTBEDVKlupQnuoyFzUSRg6FkcM7D4rjUj3Ux0DTHKhIIOkasqb7JchRf7ae57FqfR93P91C32CeYxfWsOKchTvf+nYAq3QPmnXthmvb2L1bsbq6sLq7KARje2sPXnHk8btqVRV6QyMVhQLzvv+dUnrHN24gN2sObqGAZ1kjY6uwc1qhgGv5055ljROQzyLb2IgcjiBHwv44HEaORHZM22HeT1MiERTHpfaDF6NuaKU4fwHbH3wMryzYdkHtgj+SdkjbYTpYLmXSVL3jXNTWlgktde+rQr5IeijP0GCebM92jv/spcQ62ya6RkAE+Kl20AWXQ8xL63r56f2vsXRBDZ+++Kh96q/+oNwHuwkItlukbbAds7+FdclW2oc24+GhKzrzy+dSSROF/gRtmzy6tmUJWzlu+c3naRroJhWK8cuv/5jKc05jbkWU2bEwEfXNT4ycYpZ8ahPyQ79l1hd+WEpf+4PLyC5uABeKBY1cNkQqE2UgGyObC5PPauiDWeJDvVRmuigv9KF4frWoXCyy9K9/Hrf0CICiIOs6rqKSKUrkHAlJ00hUxYjFo0ia5pckNR1J11Bcl3k3fYdQTzeF+gY2/dd1eJEISJJ/siBJo6ZlkCW/GjpYJkkSSDKSLCPl8zR/4Sr0zs1YTc20X/9tXFnFK9p4to1r+2N/sMbMD7/GwrNtpGyWox59iGhqiFx5Ba+/5xKIx5HDYb8EHAoFJ0Ah5HAkSAvmgxMjKRTyg9/wskIBXnyBTLSMwkCSQncXVlcX9rZecF1/+0kSWk0temMjesPooQElEin9ziaiutlzXRhIUvXO8/yAvGAhycf/DPH9fIjPIXiSBVDI26QG86QG/SCeHizsMG8VRk62mrpNrvzdF0vzE9U4VDSyEw55xy+ewWXnL+I3T6zn14+Z/MvbF0+PnspisV0e5Jqssigxn0WJ+VwEZO0cLQMbWNffiplsYW3WBAniRoy3HDePmg1ZQvIgAPFCmvbVvTzV9xpauY5eGWLGjCiLZyZYVBVjdjxChb7z4a+oUcoSR8IF/4735duQbAs3EiJ20scIxWWKhSSONUC0kKTCStLo9O7wfstSyebqGcrNATlOWC8nMZTGW/UsXgai6RTNp51Obu48v0Ro2xSyeVo3baN76xDhsMvs6jA1ZQrYNq5l4WUyfknStvAsm7KeLkI93QCEerrJ33XnflXp6p2bAdC3dJK94+cj61IUZE3zTzCGB9Ufy5rmB+TyciTVn492bSGaGgIgMjRIRSRCpqoKt1DASacpbt/uV18X8n6V9XCA3oWdTozOOhe1sYlQczPxE05Ab2gi1NiIVlePrL9Jl7yxGMnH/0JtbwfJ/agelmQZqqpJPvnXiQ2iuzkO9pfnecEAnhuMR6ftsGzH5aQzKGvfoK92NoO2UgreqcE8qaE8VsHZ4bNUTSZeEaa8Ikx9czmxYDpeEaZcPYbiS7eXTrImo3GoCPDCIePs5c0MpC0efn4TFbEQF58+b6qzNKmiWoSltUextPYoAPrzScz+VtYlWzD7WzEjSS4M60CW7dUxZr8rQYWj0bFZprd9iI2bhti4socnYhpahU6iJsrCpgqOqC9nbjxKTVgbOWmqq6Nv1eul6/nhujrGa+LoFnMUrQF/KCQpZLejpfoos5Ioch+y5CGHLGQNJA/yjZV0zOqnoEJRCjNUVOm1JdLVDYQbFlOWqCKpRBiUZWRZRpYlZEVCkiV/WpZIpwaZ/4+VRDMDFCIx9K/fTOPMBkIhBUWWwPP8kqbnguuNmg/+tD0XXBfP85DSaewPXIzWthF73nzqf3I7VFYiqSqSspeXgtJpio89XPoDr7zmK1TuIvh5nodXLOIVCqWA7wf/Al4hj5svoK15leijD/r7Pp3C+MSncE48ae/yNFosBnPfAhNRizWBAdnzPKztA/D662RmL8TSwhRtF9tysG0H23IoBuPh+eHp4ph5Mmkuv/0qEv2d9FU1c/uHbsLW961nQ83K8a+/vZra/k5iVc38/EM3QSxGPAjYjbMqiZUHwbvSH4fC6m4LHsl7H6H2hWdIvuWMSbl0IAK8cEh572lzGcoUePj5TVTGdM5e3jzVWZoyVeEEJzeewMmNJ+B5HgPPPU5z1y8AqN6epqf1UVoX1qHN1zhyaROVUh1uJkFPl0LnlhxdWzJ0rd7G33QZrTJELBFmbmM5R81MMK8iShNvftVLViPoagQ9unNHM57nkksnKf76F0Q29wMQ7hqgbH0PxWUhwqHtVFfazG3a8X2uK1GwQhQKIfIFnXxWp5DXyeV08gWdis4uQm4GDwjl0jx/+1NsaTD89UdUImU60TLdH0d1ImXayHwwjkQ1v5Fg7QwGHny8dCIj70/vf0EpeU9KtpLkX4ZA01BiMRzHJZMqkE1bZNIF0sUChboQiXgN8VQf6ViCVf0R1Je3oIdU9JBKKKwSCqnowVhR5d3XaqXTsPENmIgGXrupBnddj3zOJp+1yWUt8jmbXMafzpXSg/msjTs4xJV3+oF0WxBIdxWUVVVG1RQ0PRiC6UhUR9MVZnRsoaa/E4Ca/k5Ob7IYMo7wr9oEl2r8KzhjpuVgDEhSEUlyiG1YQzySId9YSTRc4PLzJThqDhCcJOKBZ+NRAG8A13LJFQCCk8lg7N8N4CJlMjRe+knY2E5iktoHiAAvHFIkSeKKCwyGMjZ3PrGe8qjO8YtnTHW2ppwkSSSWnUpx4aJSCfKjl3yLDcU+2obaaRvsYPXQSzieAzOgflYVdaEmNKuaZG8ZnZtttvfm2G4m+afcQblm8aMffpzyzAD5UJRnHn6G2cZcqkLaHl8akSSZaLwaLvkI3re+g2TbuJrG45F38+zfPGoqwnzg7HkcPSeEa6dx7FRpKJam0zh2H547cq+yfJSF9UicyOZ+8k0Jjl0xyNGRdRSLGpatYVkqhbxMNieT7JHozitYtopta9i2im2rgEQ4olKuOnzg1k9R3tfJUN0snrnht8gVcXRdRQ8p6CEVTffH+vA45AcVeQ/agXieRy5rk00XyKT84J1JFcikR02n/CA41syt64mn+gCIpZO0//EfpROZ8ciKtEPAD4XV0slAFIszvno5dLYRaZ7Hyu/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      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(8, 6))\n",
    "for maturity in maturities:\n",
    "    data = plot_data[options_data.MATURITY == maturity]\n",
    "      # select data for this maturity\n",
    "    plt.plot(data['STRIKE'], data['IMP_VOL'],\n",
    "             label=maturity.date(), lw=1.5)\n",
    "    plt.plot(data['STRIKE'], data['IMP_VOL'], 'r.', label='')\n",
    "plt.grid(True) \n",
    "plt.xlabel('strike')\n",
    "plt.ylabel('implied volatility of volatility')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "# tag: vs_imp_vol\n",
    "# title: Implied volatilities (of volatility) for European call options on the VSTOXX on 31. March 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "uuid": "34582055-b4b2-49ea-8678-e44288f7de88"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001BC9973DC88>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keep = ['PRICE', 'IMP_VOL']\n",
    "group_data = plot_data.groupby(['MATURITY', 'STRIKE'])[keep]\n",
    "group_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "uuid": "1b5253de-0b31-4af0-8155-b2c405363a0d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PRICE</th>\n",
       "      <th>IMP_VOL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MATURITY</th>\n",
       "      <th>STRIKE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2014-04-18</th>\n",
       "      <th>9.0</th>\n",
       "      <td>8.85</td>\n",
       "      <td>2.083388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>7.85</td>\n",
       "      <td>1.804193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>6.85</td>\n",
       "      <td>1.550283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>5.85</td>\n",
       "      <td>1.316103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>4.85</td>\n",
       "      <td>1.097184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PRICE   IMP_VOL\n",
       "MATURITY   STRIKE                 \n",
       "2014-04-18 9.0      8.85  2.083388\n",
       "           10.0     7.85  1.804193\n",
       "           11.0     6.85  1.550283\n",
       "           12.0     5.85  1.316103\n",
       "           13.0     4.85  1.097184"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_data = group_data.sum()\n",
    "group_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "uuid": "390e4908-23ed-4419-a62d-b25ed3cf2d15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList([[2014-04-18 00:00:00, 2014-05-16 00:00:00, 2014-06-20 00:00:00, 2014-07-18 00:00:00, 2014-08-15 00:00:00, 2014-09-19 00:00:00, 2014-10-17 00:00:00, 2014-11-21 00:00:00], [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_data.index.levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monte Carlo Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "uuid": "13a3c945-2ae9-4583-b21f-cb6d4e4729b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.021352235143176"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bsm_functions import bsm_call_value\n",
    "S0 = 100.\n",
    "K = 105.\n",
    "T = 1.0\n",
    "r = 0.05\n",
    "sigma = 0.2\n",
    "bsm_call_value(S0, K, T, r, sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "uuid": "aebfedcc-472c-4225-82eb-6405ecf7eaa9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "European Option Value   7.999\n",
      "Duration in Seconds    17.055\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run mcs_pure_python.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "uuid": "d038ec31-373b-4979-aa49-a47ff96fc234"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.999"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_val = 0.0\n",
    "for path in S:\n",
    "    # C-like iteration for comparison\n",
    "    sum_val += max(path[-1] - K, 0)\n",
    "C0 = exp(-r * T) * sum_val / I\n",
    "round(C0, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorization with NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "uuid": "2f695395-b7b7-4d0c-bab3-659fcd4da5d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "v = list(range(1, 6))\n",
    "print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "uuid": "548c6e07-9435-421c-9408-d63252608f74"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "uuid": "0a831df5-d2c3-46af-8399-ad2b4b411001"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "v = np.arange(1, 6)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "uuid": "4c5153ff-983f-4ee1-95d6-0dae809da4bf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "uuid": "0fca4476-2003-4733-a6e1-f405997e1b1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "European Option Value   8.020\n",
      "Duration in Seconds     1.498\n"
     ]
    }
   ],
   "source": [
    "%run mcs_vector_numpy.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "uuid": "6d10a22b-fed3-4627-a2bc-6f958824ca2f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.39"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(tpy / tnp1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Full Vectorization with Log Euler Scheme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "uuid": "02a121e5-75ff-4ad1-9812-ec0e69d3ebaa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "European Option Value   8.023\n",
      "Duration in Seconds     1.695\n"
     ]
    }
   ],
   "source": [
    "%run mcs_full_vector_numpy.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "uuid": "f880bd6e-72fa-4906-b2a7-24f3c7419338"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'index level')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(S[:, :10])\n",
    "plt.grid(True)\n",
    "plt.xlabel('time step')\n",
    "plt.ylabel('index level')\n",
    "# tag: index_paths\n",
    "# title: The first 10 simulated index level paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "uuid": "997e2c48-f4e4-49c2-be9b-ce2ed0a2166f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(S[-1], bins=50)\n",
    "plt.grid(True)\n",
    "plt.xlabel('index level')\n",
    "plt.ylabel('frequency')\n",
    "# tag: index_histo\n",
    "# title: Histogram of all simulated end of period index level values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "uuid": "bc4f9174-c92c-4abf-9d31-da3e7d0baec5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 50000.0)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.maximum(S[-1] - K, 0), bins=50)\n",
    "plt.grid(True)\n",
    "plt.xlabel('option inner value')\n",
    "plt.ylabel('frequency')\n",
    "plt.ylim(0, 50000)\n",
    "# tag: option_iv_hist\n",
    "# title: Histogram of all simulated end of period option inner values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "uuid": "d5aed456-001a-423c-9366-df294f1c3f0b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "268632"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(S[-1] < K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Technical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "uuid": "dcba1a61-94e3-47dd-ab54-2c688e740741"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "uuid": "3b6ce702-6a8c-400c-a6fc-d382f316dd9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1972 entries, 2010-01-04 to 2017-10-31\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Close   1972 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 30.8 KB\n"
     ]
    }
   ],
   "source": [
    "raw = pd.read_csv('source/tr_eikon_eod_data.csv',\n",
    "                   index_col=0, parse_dates=True)\n",
    "AAPL = pd.DataFrame(raw['AAPL.O'])\n",
    "AAPL.columns = ['Close']\n",
    "AAPL.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "uuid": "444a17d9-8fd1-436d-825a-3001cb080b7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc98b74048>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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111.806089 \r\nL 377.153424 112.701902 \r\nL 377.295468 111.130301 \r\nL 377.721598 109.354392 \r\nL 377.863642 111.413189 \r\nL 378.005685 109.605848 \r\nL 378.147728 113.409122 \r\nL 378.715902 112.654754 \r\nL 378.999989 107.908519 \r\nL 379.142033 107.814223 \r\nL 379.284076 111.350325 \r\nL 379.85225 115.970832 \r\nL 379.994294 115.625079 \r\nL 380.27838 117.903901 \r\nL 380.704511 118.846861 \r\nL 380.846554 119.99413 \r\nL 381.130641 134.95577 \r\nL 381.272685 136.668814 \r\nL 381.698815 136.825974 \r\nL 381.840859 134.405709 \r\nL 382.266989 138.271847 \r\nL 382.693119 138.161835 \r\nL 382.835163 137.171727 \r\nL 382.977206 138.601883 \r\nL 383.11925 142.012257 \r\nL 383.261293 141.729369 \r\nL 383.687424 136.44879 \r\nL 383.829467 137.061715 \r\nL 383.971511 135.380102 \r\nL 384.113554 135.945878 \r\nL 384.255597 134.342845 \r\nL 384.681728 132.441208 \r\nL 385.107858 126.186237 \r\nL 385.249902 126.280533 \r\nL 385.818076 127.050617 \r\nL 386.102163 130.413843 \r\nL 386.670336 128.983686 \r\nL 386.81238 128.355046 \r\nL 386.954423 128.49649 \r\nL 387.096467 127.380654 \r\nL 387.23851 128.669366 \r\nL 387.664641 131.011051 \r\nL 387.806684 130.822459 \r\nL 387.948728 131.325372 \r\nL 388.090771 130.681015 \r\nL 388.232815 134.169969 \r\nL 388.658945 134.531437 \r\nL 388.800988 133.258441 \r\nL 388.943032 133.824217 \r\nL 389.085075 132.959836 \r\nL 389.227119 137.203159 \r\nL 389.653249 139.340536 \r\nL 390.07938 133.745637 \r\nL 390.221423 133.289873 \r\nL 390.789597 134.704313 \r\nL 391.215727 132.048308 \r\nL 391.641858 131.576828 \r\nL 391.783901 130.885323 \r\nL 391.925945 131.749704 \r\nL 392.067988 128.73223 \r\nL 392.210032 128.747946 \r\nL 392.636162 127.097765 \r\nL 392.920249 126.893457 \r\nL 393.77251 132.064024 \r\nL 394.056597 120.009846 \r\nL 394.19864 120.214154 \r\nL 394.62477 117.322408 \r\nL 394.766814 119.789822 \r\nL 394.908857 117.731025 \r\nL 395.050901 117.605296 \r\nL 395.192944 115.075019 \r\nL 395.761118 112.98479 \r\nL 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7.2 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n   <g id=\"patch_5\">\r\n    <path d=\"M 33.2875 252 \r\nL 479.6875 252 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n   <g id=\"patch_6\">\r\n    <path d=\"M 33.2875 7.2 \r\nL 479.6875 7.2 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n  </g>\r\n </g>\r\n <defs>\r\n  <clipPath id=\"pde9d50150b\">\r\n   <rect height=\"244.8\" width=\"446.4\" x=\"33.2875\" y=\"7.2\"/>\r\n  </clipPath>\r\n </defs>\r\n</svg>\r\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "AAPL['Close'].plot(grid=True, figsize=(8, 5))\n",
    "# tag: AAPL\n",
    "# title: Historical levels of the S&P 500 index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "uuid": "d134fa6d-4db4-40f3-b1ce-952ef2346e18"
   },
   "outputs": [],
   "source": [
    "AAPL['42d'] = np.round(AAPL['Close'].rolling(window=42).mean(), 2)\n",
    "AAPL['252d'] = np.round(AAPL['Close'].rolling(window=252).mean(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "uuid": "8af03824-0abb-4747-9f8f-d517d48bd673"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>42d</th>\n",
       "      <th>252d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>156.41</td>\n",
       "      <td>157.61</td>\n",
       "      <td>139.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>157.41</td>\n",
       "      <td>157.51</td>\n",
       "      <td>140.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>163.05</td>\n",
       "      <td>157.52</td>\n",
       "      <td>140.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>166.72</td>\n",
       "      <td>157.60</td>\n",
       "      <td>140.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>169.04</td>\n",
       "      <td>157.72</td>\n",
       "      <td>140.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Close     42d    252d\n",
       "Date                              \n",
       "2017-10-25  156.41  157.61  139.86\n",
       "2017-10-26  157.41  157.51  140.03\n",
       "2017-10-27  163.05  157.52  140.22\n",
       "2017-10-30  166.72  157.60  140.43\n",
       "2017-10-31  169.04  157.72  140.65"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AAPL[['Close', '42d', '252d']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "uuid": "4dc6ac7e-6fac-4994-811b-35789c551de7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bcbc11f948>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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197.792947 \r\nL 140.50901 197.745799 \r\nL 140.651054 199.241063 \r\nL 141.077184 198.688758 \r\nL 142.071488 191.573904 \r\nL 142.213532 191.165288 \r\nL 142.355575 191.459402 \r\nL 142.497619 193.776388 \r\nL 142.639662 193.219593 \r\nL 143.065793 193.473294 \r\nL 143.491923 196.302173 \r\nL 143.633966 198.378929 \r\nL 144.20214 200.359144 \r\nL 144.344184 199.068187 \r\nL 144.486227 199.26576 \r\nL 144.628271 200.965332 \r\nL 145.054401 196.697318 \r\nL 145.196445 194.119895 \r\nL 145.338488 193.693318 \r\nL 145.480531 192.292349 \r\nL 145.622575 189.245692 \r\nL 146.048705 189.696965 \r\nL 146.190749 189.191809 \r\nL 146.332792 194.494833 \r\nL 146.616879 195.78579 \r\nL 147.04301 192.889557 \r\nL 147.185053 194.685671 \r\nL 147.611183 193.073659 \r\nL 148.037314 193.111826 \r\nL 148.179357 194.968558 \r\nL 148.321401 194.766496 \r\nL 148.463444 193.495745 \r\nL 148.605488 194.131121 \r\nL 149.031618 194.245623 \r\nL 149.173662 192.786281 \r\nL 149.457749 197.503323 \r\nL 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179.061729 \r\nL 240.791697 172.070357 \r\nL 240.93374 172.202821 \r\nL 241.075784 171.210468 \r\nL 241.501914 169.995846 \r\nL 241.643958 171.493356 \r\nL 241.928045 171.069024 \r\nL 242.070088 171.504582 \r\nL 242.496219 171.066779 \r\nL 242.638262 174.295293 \r\nL 242.922349 173.597054 \r\nL 243.064392 174.603775 \r\nL 243.632566 174.297538 \r\nL 243.77461 172.027475 \r\nL 243.916653 172.795538 \r\nL 244.484827 170.348334 \r\nL 244.626871 172.936982 \r\nL 244.768914 178.983149 \r\nL 244.910958 177.865068 \r\nL 245.053001 179.614034 \r\nL 245.479131 182.932353 \r\nL 245.621175 181.76488 \r\nL 245.905262 177.952629 \r\nL 246.047305 179.050503 \r\nL 246.473436 173.835039 \r\nL 246.615479 174.180791 \r\nL 246.757523 175.880363 \r\nL 246.899566 174.827391 \r\nL 247.04161 175.606456 \r\nL 247.46774 176.953541 \r\nL 247.609783 174.436737 \r\nL 247.751827 174.077514 \r\nL 247.89387 175.458276 \r\nL 248.035914 175.543592 \r\nL 248.462044 174.483885 \r\nL 248.604088 176.012826 \r\nL 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136.118754 \r\nL 368.346729 131.011051 \r\nL 368.77286 132.441208 \r\nL 368.914903 135.50583 \r\nL 369.056947 132.566936 \r\nL 369.19899 132.174036 \r\nL 369.341034 136.228766 \r\nL 369.767164 134.672881 \r\nL 369.909207 134.704313 \r\nL 370.193294 136.731678 \r\nL 370.335338 136.275914 \r\nL 370.903512 132.111172 \r\nL 371.045555 129.785203 \r\nL 371.187599 132.70838 \r\nL 371.329642 133.054133 \r\nL 371.755773 131.733988 \r\nL 371.897816 135.175794 \r\nL 372.181903 131.92258 \r\nL 372.323946 131.68684 \r\nL 372.750077 132.032592 \r\nL 372.89212 125.997645 \r\nL 373.034164 125.651893 \r\nL 373.176207 124.473192 \r\nL 373.318251 122.100075 \r\nL 373.744381 123.8917 \r\nL 373.886424 125.211844 \r\nL 374.170511 124.99182 \r\nL 374.312555 123.278775 \r\nL 374.738685 122.870159 \r\nL 375.022772 117.448136 \r\nL 375.164816 117.715309 \r\nL 375.306859 117.526716 \r\nL 375.73299 117.542432 \r\nL 375.875033 116.269436 \r\nL 376.15912 117.919617 \r\nL 376.727294 118.673985 \r\nL 377.011381 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128.307898 \r\nL 298.461344 127.94643 \r\nL 298.745431 127.899282 \r\nL 299.171561 127.820702 \r\nL 299.739735 127.192061 \r\nL 300.307909 126.846309 \r\nL 300.73404 126.264817 \r\nL 301.444257 126.123373 \r\nL 301.728344 126.343397 \r\nL 302.296518 126.311965 \r\nL 302.580605 126.123373 \r\nL 302.722648 125.950497 \r\nL 303.148779 125.840485 \r\nL 303.574909 125.353289 \r\nL 303.716952 125.023252 \r\nL 304.143083 124.59892 \r\nL 304.711257 123.137331 \r\nL 305.137387 122.870159 \r\nL 305.563517 121.754323 \r\nL 305.705561 121.282842 \r\nL 306.131691 120.827078 \r\nL 306.699865 118.721133 \r\nL 307.125996 117.935333 \r\nL 307.694169 115.923684 \r\nL 308.1203 115.326475 \r\nL 308.688474 112.969074 \r\nL 309.114604 112.529026 \r\nL 309.682778 110.768833 \r\nL 310.108908 110.360217 \r\nL 310.677082 108.710036 \r\nL 311.103213 108.332852 \r\nL 311.3873 107.798507 \r\nL 311.671386 107.452755 \r\nL 312.23956 106.996991 \r\nL 312.381604 106.871263 \r\nL 312.665691 106.824115 \r\nL 313.517952 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349.312905 102.486497 \r\nL 349.454948 103.130853 \r\nL 349.881079 103.508037 \r\nL 350.449253 104.938194 \r\nL 350.875383 105.331094 \r\nL 351.301513 106.572659 \r\nL 351.443557 106.745535 \r\nL 352.011731 107.059855 \r\nL 352.153774 107.232731 \r\nL 352.437861 107.672779 \r\nL 353.006035 107.924235 \r\nL 353.432165 108.552876 \r\nL 354.000339 108.631456 \r\nL 354.284426 107.861371 \r\nL 354.42647 107.295595 \r\nL 354.994644 107.012707 \r\nL 355.27873 106.509795 \r\nL 355.420774 106.069746 \r\nL 355.846904 105.73971 \r\nL 356.415078 104.073813 \r\nL 357.125296 103.649481 \r\nL 357.267339 103.665197 \r\nL 357.409382 103.822357 \r\nL 358.1196 103.728061 \r\nL 358.403687 103.335161 \r\nL 358.971861 103.083705 \r\nL 359.397991 102.533645 \r\nL 359.824121 102.23504 \r\nL 360.392295 101.276364 \r\nL 360.818426 100.993476 \r\nL 361.102513 100.537712 \r\nL 361.3866 100.317687 \r\nL 362.096817 100.270539 \r\nL 362.23886 100.364835 \r\nL 362.380904 100.66344 \r\nL 362.807034 100.89918 \r\nL 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      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "AAPL[['Close', '42d', '252d']].plot(grid=True, figsize=(8, 5))\n",
    "# tag: AAPL_trend\n",
    "# title: The Apple, Inc. stock price with 42d and 252d trend lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "uuid": "ccc2b0e9-5ab7-49c1-8054-3a1e1b23ab92"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2017-10-25    17.75\n",
       "2017-10-26    17.48\n",
       "2017-10-27    17.30\n",
       "2017-10-30    17.17\n",
       "2017-10-31    17.07\n",
       "Name: 42-252, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AAPL['42-252'] = AAPL['42d'] - AAPL['252d']\n",
    "AAPL['42-252'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "uuid": "e127f7c9-8e7f-4038-b490-502e6bf436f7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2010-01-04   NaN\n",
       "2010-01-05   NaN\n",
       "2010-01-06   NaN\n",
       "2010-01-07   NaN\n",
       "2010-01-08   NaN\n",
       "Name: 42-252, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AAPL['42-252'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "AAPL.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "uuid": "d1125e1b-3cd0-4274-aee7-9b1136db8c02"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    1260\n",
       "-1     445\n",
       " 0      16\n",
       "Name: Position, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SD = 0.5\n",
    "AAPL['Position'] = np.where(AAPL['42-252'] > SD, 1, 0)\n",
    "AAPL['Position'] = np.where(AAPL['42-252'] < -SD, -1, AAPL['Position'])\n",
    "AAPL['Position'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "uuid": "ffec4dc1-ebb4-43e1-962d-1fbc465afee1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "AAPL['Position'].plot(lw=1.5, grid=True)\n",
    "plt.ylim([-1.1, 1.1]);\n",
    "# tag: AAPL_signal\n",
    "# title: Positions over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "uuid": "0e51676f-7b84-441d-8191-08799247774c"
   },
   "outputs": [],
   "source": [
    "AAPL['Market'] = np.log(AAPL['Close'] / AAPL['Close'].shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "uuid": "25f3c42a-ec95-46e3-a194-4e29f0c830af"
   },
   "outputs": [],
   "source": [
    "AAPL['Strategy'] = AAPL['Position'].shift(1) * AAPL['Market']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "uuid": "ce759e67-918d-432c-a411-b22c6777dac5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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189.736338 212.896558 \r\nL 190.061904 214.9441 \r\nL 190.550253 214.610691 \r\nL 190.875819 219.487496 \r\nL 191.038603 218.408534 \r\nL 191.201386 218.6568 \r\nL 191.689735 216.835298 \r\nL 191.852518 217.451256 \r\nL 192.340867 216.417125 \r\nL 192.991999 217.116152 \r\nL 193.154782 216.454798 \r\nL 193.317565 215.205929 \r\nL 193.480348 215.553465 \r\nL 193.968698 215.367924 \r\nL 194.131481 215.633521 \r\nL 194.457047 217.677296 \r\nL 194.61983 217.04608 \r\nL 195.270962 217.839291 \r\nL 195.433745 218.858352 \r\nL 195.596528 218.14727 \r\nL 195.759311 219.261456 \r\nL 196.247661 218.894142 \r\nL 196.410444 218.937466 \r\nL 196.73601 221.750153 \r\nL 197.387142 224.443415 \r\nL 197.549925 224.426462 \r\nL 197.875491 226.093504 \r\nL 198.038274 225.575496 \r\nL 198.526624 223.185127 \r\nL 198.689407 221.438971 \r\nL 198.85219 221.003845 \r\nL 199.177756 221.640523 \r\nL 199.666105 222.086952 \r\nL 199.828888 220.711877 \r\nL 199.991671 221.017031 \r\nL 200.154454 219.781724 \r\nL 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164.930816 \r\nL 271.779003 166.10434 \r\nL 271.941786 171.220375 \r\nL 272.104569 167.422905 \r\nL 272.592918 168.266787 \r\nL 272.755701 167.422905 \r\nL 272.918484 169.493054 \r\nL 273.081267 168.543686 \r\nL 273.244051 168.912885 \r\nL 273.7324 168.912885 \r\nL 273.895183 170.060037 \r\nL 274.057966 167.356977 \r\nL 274.220749 167.066893 \r\nL 274.871881 168.662357 \r\nL 275.36023 173.343266 \r\nL 275.523013 171.484088 \r\nL 276.011363 168.728285 \r\nL 276.174146 165.154972 \r\nL 276.336929 164.469318 \r\nL 276.499712 162.043157 \r\nL 276.662495 161.528917 \r\nL 277.150844 161.673959 \r\nL 277.47641 158.733557 \r\nL 277.639193 159.208241 \r\nL 277.801976 157.863304 \r\nL 278.290326 156.017312 \r\nL 278.453109 157.072164 \r\nL 278.615892 156.729337 \r\nL 278.778675 156.940308 \r\nL 278.941458 156.531552 \r\nL 279.429807 156.768894 \r\nL 279.59259 155.621742 \r\nL 280.080939 149.714568 \r\nL 280.569288 149.965095 \r\nL 280.732072 148.013618 \r\nL 280.894855 149.06847 \r\nL 281.057638 146.906023 \r\nL 281.220421 146.695052 \r\nL 281.70877 143.853543 \r\nL 281.871553 145.205073 \r\nL 282.034336 143.359081 \r\nL 282.359902 143.45138 \r\nL 283.011034 149.121213 \r\nL 283.173817 147.407077 \r\nL 283.499384 148.633344 \r\nL 283.987733 152.061615 \r\nL 284.150516 149.793681 \r\nL 284.313299 152.654969 \r\nL 284.476082 153.090096 \r\nL 284.638865 155.582185 \r\nL 285.127214 157.566626 \r\nL 285.289997 159.518104 \r\nL 285.615563 151.731973 \r\nL 285.778347 152.879125 \r\nL 286.266696 151.349589 \r\nL 286.592262 152.575855 \r\nL 286.917828 149.965095 \r\nL 287.406177 150.07058 \r\nL 287.731743 154.725117 \r\nL 288.057309 156.109611 \r\nL 288.545659 160.170794 \r\nL 288.708442 160.157608 \r\nL 288.871225 158.192945 \r\nL 289.034008 152.734083 \r\nL 289.196791 152.575855 \r\nL 289.68514 156.215097 \r\nL 289.847923 154.936088 \r\nL 290.010706 155.489885 \r\nL 290.173489 159.419211 \r\nL 290.336272 160.513621 \r\nL 291.150188 155.819527 \r\nL 291.312971 152.061615 \r\nL 291.475754 151.296846 \r\nL 291.964103 151.138619 \r\nL 292.126886 156.360139 \r\nL 292.452452 143.490937 \r\nL 292.615235 145.785242 \r\nL 293.103584 143.84695 \r\nL 293.266368 143.820579 \r\nL 293.591934 142.119629 \r\nL 293.754717 143.45138 \r\nL 294.243066 142.409713 \r\nL 294.731415 133.52258 \r\nL 294.894198 132.70507 \r\nL 295.54533 131.716145 \r\nL 295.708114 130.549215 \r\nL 295.870897 130.898634 \r\nL 296.522029 124.89916 \r\nL 296.684812 125.99357 \r\nL 296.847595 130.450322 \r\nL 297.010378 128.307653 \r\nL 297.173161 130.885449 \r\nL 297.824293 129.69874 \r\nL 297.987076 130.779964 \r\nL 298.149859 133.588509 \r\nL 298.800992 132.625956 \r\nL 299.126558 139.086928 \r\nL 299.289341 136.172897 \r\nL 299.452124 137.306864 \r\nL 299.940473 135.513615 \r\nL 300.266039 130.872263 \r\nL 300.591605 134.260977 \r\nL 301.079955 132.533656 \r\nL 301.242738 133.21931 \r\nL 301.405521 137.583763 \r\nL 301.568304 136.449796 \r\nL 301.731087 137.755176 \r\nL 302.219436 133.641251 \r\nL 302.382219 136.199269 \r\nL 302.545002 136.436611 \r\nL 302.707785 135.025745 \r\nL 303.358918 132.349057 \r\nL 303.521701 134.115935 \r\nL 303.684484 134.656547 \r\nL 304.01005 132.678698 \r\nL 304.498399 133.00834 \r\nL 304.661182 133.733551 \r\nL 304.823965 133.100639 \r\nL 304.986748 133.904964 \r\nL 305.149531 135.777328 \r\nL 305.63788 132.019415 \r\nL 305.800664 132.929226 \r\nL 306.12623 129.289984 \r\nL 306.289013 128.485659 \r\nL 306.777362 125.360658 \r\nL 307.265711 135.249901 \r\nL 307.428494 130.239352 \r\nL 307.916843 130.568993 \r\nL 308.079626 134.392834 \r\nL 308.24241 135.434501 \r\nL 308.405193 135.104859 \r\nL 308.567976 131.993044 \r\nL 309.219108 134.307127 \r\nL 309.381891 134.115935 \r\nL 309.544674 130.239352 \r\nL 309.707457 130.476693 \r\nL 310.195806 128.60433 \r\nL 310.521372 128.775744 \r\nL 310.846939 125.505701 \r\nL 311.498071 129.355913 \r\nL 311.660854 126.158391 \r\nL 311.823637 126.507811 \r\nL 311.98642 128.485659 \r\nL 312.474769 128.149425 \r\nL 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111.720634 \r\nL 318.823309 106.679922 \r\nL 319.311658 102.942571 \r\nL 319.474441 103.021087 \r\nL 320.451139 92.87685 \r\nL 320.613923 94.949666 \r\nL 320.776706 103.63351 \r\nL 320.939489 103.727729 \r\nL 321.590621 107.480783 \r\nL 321.753404 106.52289 \r\nL 322.07897 108.108909 \r\nL 322.730102 114.280248 \r\nL 322.892885 120.247447 \r\nL 323.055668 119.054007 \r\nL 323.218452 119.477992 \r\nL 323.381235 118.865569 \r\nL 323.869584 112.270245 \r\nL 324.032367 122.05331 \r\nL 324.19515 119.305258 \r\nL 324.357933 119.446586 \r\nL 324.520716 118.174631 \r\nL 325.009065 116.290252 \r\nL 325.171848 117.32666 \r\nL 325.497414 123.372375 \r\nL 325.660198 134.191848 \r\nL 326.148547 138.33748 \r\nL 326.31133 137.363885 \r\nL 326.636896 122.948389 \r\nL 326.799679 122.367373 \r\nL 327.288028 123.19964 \r\nL 327.450811 131.114029 \r\nL 327.613594 123.859172 \r\nL 327.776377 126.952694 \r\nL 328.915859 126.952694 \r\nL 329.078642 129.441423 \r\nL 329.566991 131.071027 \r\nL 329.729774 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      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "AAPL[['Market', 'Strategy']].cumsum().apply(np.exp).plot(grid=True,\n",
    "                                                    figsize=(8, 5));\n",
    "# tag: AAPL_wealth\n",
    "# title: The Apple stock performance vs. investor's wealth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>\n",
    "\n",
    "**Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://python-for-finance.com\" target=\"_blank\">Python for Finance @ O'Reilly</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Listed Volatility and Variance Derivatives** |\n",
    "<a href=\"http://lvvd.tpq.io\" target=\"_blank\">Listed VV Derivatives @ Wiley Finance</a>\n",
    "\n",
    "**Python Training** |\n",
    "<a href=\"http://training.tpq.io\" target=\"_blank\">Python for Finance University Certificate</a>"
   ]
  }
 ],
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